What Two Decades of Environmental-Economic Accounting Reveal About Guatemala’s Natural Capital and Governance

What Two Decades of Environmental-Economic Accounting Reveal About Guatemala’s Natural Capital and Governance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article What Two Decades of Environmental-Economic Accounting Reveal About Guatemala’s Natural Capital and Governance Juan Pablo Castaneda This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9438795/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Guatemala is one of the few developing countries to have sustained more than two decades of continuous of the system of environmental-economic accounting (SEEA). This paper synthesizes the accumulated record and draws out its lessons learned. Empirically, the accounts revealed a structural dependency on natural capital invisible to conventional statistics. For example, forest contributions to the economy are underestimated by more than threefold; most indicators show that Guatemala has repeatedly consumed its natural asset base rather than building it; and ecosystem services generate an estimated USD 843 million in economic value each year that never enters national accounts. The paper documents how a sustained public-academic partnership translated accounts into official statistics and embedded them in national development planning, climate policy, and forest governance. Four conditions emerge as necessary for natural capital accounts to influence governance in a developing country context: stable institutional anchoring, demand-side alignment with live policy questions, staged sequencing of account complexity, and capacity building embedded within production. This trajectory culminated in the Central Bank of Guatemala creating a permanent environmental accounting unit within the macroeconomic statistics department. Also, it earned Guatemala recognition in the 2025 United Nations Statistics Department Global Assessment as a Stage III SEEA implementer, among the top tier of developing countries. Natural capital SEEA Ecosystem accounting Ecosystem services Environmental-economic accounting Guatemala Natural capital accounting 1. Introduction Accelerating ecosystem degradation has intensified scrutiny of prevailing development models. Over the past century, economic growth has driven unprecedented increases in resource extraction, land transformation, and pollution, generating welfare gains while simultaneously contributing to biodiversity loss, climate change, soil degradation, and the erosion of ecosystem services on which human well-being depends (Costanza et al., 2014 ; Dasgupta, 2021 ). A fundamental driver of this disconnect is the design of conventional economic indicators, which prioritize short-term production and consumption while leaving the ecological foundations of economic activity unmeasured and, consequently, unmanaged. Closing this gap requires statistical frameworks that treat natural capital as a measurable and depletable asset within national accounting systems. Gross domestic product (GDP) and related indicators measure economic activity through market transactions, providing detailed information on production, investment, and consumption, but omit the contributions of ecosystems and natural resources. Environmental degradation can therefore erode the productive capacity of economies without registering in national accounts (Bartelmus, 2009 ; Stiglitz et al., 2009 ). This measurement gap has stimulated interest in the concept of natural capital, defined as the stock of ecosystems and natural resources that generate flows of goods and services underpinning economic production and human well-being (Costanza et al., 1997 ; Millennium Ecosystem Assessment, 2005 ). Addressing this gap requires moving away from GDP as the sole measure of economic performance toward broader frameworks that integrate natural capital alongside produced and human capital (World Bank, 2024 ). Ecosystem services research has established that nature generates a broad spectrum of economic and social benefits: provisioning services such as food, timber, and freshwater; regulating services including climate regulation, flood mitigation, and water purification; and cultural services encompassing recreation, tourism, and cultural identity (Diaz et al., 2018). Because most of these services flow outside markets, they remain invisible in conventional statistics, creating incentives that systematically undervalue and degrade ecosystems. Ecological economics underscores this problem by framing economic systems as fundamentally embedded in, and constrained by, biophysical limits (Daly, 1996 ). Integrating ecological data into economic analysis is therefore not merely useful; it is essential for assessing the sustainability of development and for designing policies that safeguard the natural foundations of economic activity. In response to these challenges, international statistical organizations have developed frameworks to integrate environmental information into national accounting systems, with the System of Environmental-Economic Accounting (SEEA) being the best-known. The SEEA provides a standardized framework for linking environmental data with the System of National Accounts (SNA) and for compiling accounts for environmental assets such as forests, water resources, and energy, among others (United Nations, 2012 ). The SEEA Central Framework (SEEA-CF), adopted as an international statistical standard in 2012, establishes the foundational structure for this integration by focusing primarily on individual environmental assets and their interactions with the economy, providing the accounting architecture on which subsequent extensions have been built (United Nations et al., 2014). Building on this framework, the SEEA - Ecosystem Accounting (SEEA-EA) was adopted as an international statistical standard in 2021 (United Nations, 2021 ). The SEEA-EA framework treats ecosystems as spatial assets that generate flows of ecosystem services over time and organizes ecological information into ecosystem extent accounts, ecosystem condition accounts, and ecosystem services accounts (Edens et al., 2022 ). By integrating ecological and economic information within a consistent statistical structure, ecosystem accounting provides a mechanism for incorporating natural capital into economic analysis and policy discussions. Recent research highlights the growing role of ecosystem accounting as a tool for linking environmental sustainability with economic decision-making. Applications of the SEEA-EA framework in several countries demonstrate its potential to support environmental governance, land-use planning, and natural resource management (Comte et al., 2022 ; Hein et al., 2020 ). However, empirical applications in tropical developing economies remain relatively limited. This gap is particularly important because many developing countries depend heavily on ecosystem services associated with agriculture, forestry, fisheries, and tourism. Expanding empirical evidence on ecosystem accounting in these contexts can therefore provide valuable insights into the relationship between ecosystems and economic development. Guatemala is not merely a convenient case study; it is one of the pioneers of environmental-economic accounting among developing nations. The country's accounting program spans more than two decades, beginning in the early 2000s with a public-academic partnership between INE, Banguat, and IARNA-URL, with early support from the Dutch government (INE, Banguat, and IARNA-URL, 2013). This partnership produced SEEA Central Framework accounts covering forests, water, energy, emissions, fisheries, land, and subsoil assets for the period 2001–2010, compiled as official national statistics and published in a comprehensive statistical compendium in 2013. Building on this foundation, Guatemala formally joined the Wealth Accounting and the Valuation of Ecosystem Services (WAVES) Global Partnership in March 2014, extending and institutionalizing the accounting work through 2019 and adding ecosystem accounts under the emerging SEEA-EA framework (World Bank, 2019 ). The program entered a third and ongoing phase when Banguat formally incorporated environmental-economic accounting into its Institutional Strategic Plan 2022–2026, creating a dedicated Environmental Accounting Unit within the Departamento de Estadísticas Macroeconómicas and staffing it with a multidisciplinary team of approximately twelve professionals from economics, ecology, geography, and statistics (Banguat, 2022 ). This institutional consolidation earned Guatemala recognition as a Stage III SEEA implementer in the 2025 United Nations Statistics Division Global Assessment, placing it among the top tier of developing countries in environmental-economic accounting. The country hosts a wide range of ecosystems, including tropical rainforests, dry forests, wetlands, and montane formations, whose services underpin agriculture, hydropower, forestry, and tourism, making the integration of natural capital into economic planning both ecologically warranted and economically urgent. Despite this rich institutional history, the results of Guatemala's accounting program have been documented primarily in technical and policy reports rather than in peer-reviewed academic literature. This gap matters: translating national accounting experience into scholarly research strengthens both the theoretical foundations and the practical case for investing in natural capital measurement in similar economies. The present paper addresses this gap by making two interconnected contributions. The first is empirical as it synthesizes two decades of accounting results spanning both the SEEA Central Framework (SEEA-CF), which documents physical and monetary flows across major natural asset categories, and the SEEA Ecosystem Accounts (SEEA-EA), which characterize ecosystem extent, condition, and service supply across thirteen Holdridge life zones. The second contribution is about governance and process, documenting how the accounting program was built and institutionalized, and how it was connected to policy decisions over time, and offering lessons on the conditions under which natural capital accounts inform governance in a developing country context. These two dimensions are inseparable; the empirical record only became available because of the institutional architecture that sustained it, and the governance lessons are only credible because the accounts generated consistent, policy-relevant evidence over two decades (Castañeda et al., 2017 ; Vardon et al., 2018 ). 2. Conceptual Framework Environmental accounting is grounded in the recognition that economies operate as subsystems of a finite biosphere, constrained by biophysical laws that conventional economic indicators were not designed to capture. Ecological economics, consolidated from the foundational contributions of Daly ( 1996 ) and Costanza et al. ( 1997 ), treats economic systems as embedded within ecological systems rather than separate from them. This framing has direct implications for measurement: if economic activity depletes the ecological foundations on which it depends, a GDP-centric accounting framework will systematically misrepresent the sustainability of that growth (Daly, 1996 ; Dasgupta, 2021 ). The key conceptual distinction is between weak sustainability, which assumes that natural and produced capital are substitutable, and strong sustainability, which recognizes that certain ecological functions are irreplaceable and that some degradation thresholds are irreversible (Castañeda & Pinillos, forthcoming). Natural capital, defined as the stock of ecosystems and natural resources that generate flows of goods and services underpinning economic production and human well-being, constitutes the analytical bridge between ecological and economic frameworks (Costanza et al., 1997 ; Millennium Ecosystem Assessment, 2005 ). Ecosystem services span provisioning services such as food and freshwater, regulating services including climate stabilization and watershed regulation, and cultural services encompassing recreation and spiritual values (Díaz et al., 2018 ). Because most of these services flow outside markets, they remain invisible in conventional national accounts, generating incentives that systematically undervalue and degrade ecosystems. The Adjusted Net Savings indicator partially closes this gap by correcting standard savings measures for natural capital depletion and pollution damage, offering a first approximation of whether an economy is building or consuming its asset base (World Bank, 2024 ). Environmental-economic accounting operationalizes these concepts within a statistically rigorous framework. The SEEA provides the methodological architecture for linking ecological data to national accounts, enabling systematic and consistent measurement of how natural assets are extracted, used, and degraded in relation to economic activity. This approach is fundamentally different from isolated environmental indicators because it integrates ecological and economic information within the same statistical structure, making trade-offs and dependencies visible to policymakers (Vardon et al., 2018 ). The transition from measuring individual natural assets under the SEEA Central Framework to measuring ecosystems as integrated spatial assets under the SEEA Ecosystem Accounts represents an advance in ecological precision: asset-by-asset accounts reveal sectoral pressures, while ecosystem accounts reveal the functional interdependencies across landscapes that determine the long-term supply of services (Edens et al., 2022 ). The methodological challenges of this transition, including spatial unit definition, service prioritization, and physical-to-monetary linkage under data-scarce conditions, are precisely those that Guatemala’s program confronted and resolved over two decades of practice (Castañeda & Pinillos, forthcoming; Castañeda et al., 2019 ). The institutional dimension of accounting is as important as the technical one. Accounts are not self-executing; they require institutional embedding to translate measurement into governance. Research on the conditions under which natural capital accounts influence policy consistently identifies demand-side alignment, political ownership, and capacity continuity as critical success factors (Ruijs et al., 2019 ; Vardon et al., 2018 ). In developing country contexts, where statistical capacity is constrained and political cycles are short, sustaining an accounting program long enough to generate policy-relevant evidence requires deliberate institutional architecture. Guatemala’s experience offers a case study in how this architecture can be built and sustained, providing a basis for examining both the empirical content of two decades of accounts and the governance lessons that record contains (Castañeda & Pinillos, forthcoming). 3. Methods and Data Guatemala's environmental-economic accounting program has unfolded across three broadly distinguishable phases spanning more than two decades. The first phase (approximately 2006–2013) was initiated through a public-academic partnership between INE, Banguat, and IARNA-URL, with financial support from the Dutch government. This partnership applied the SEEA Central Framework methodology to compile accounts for forests, water, energy and emissions, fisheries, subsoil assets, land, and ecosystems. The resulting work covered the period 2001–2006 initially and later extended to 2010. Results for 2001–2006 were published in several sets of publications and dissemination documents (see Annex for a full list of publications), and the culmination of this work was published as the Sistema de Contabilidad Ambiental y Economica de Guatemala 2001–2010, a comprehensive statistical compendium constituting one of the most complete SEEA Central Framework implementations in the developing world at that time (INE, Banguat and IARNA-URL, 2013). The second phase (2014–2019) was formalized through Guatemala's participation in the Wealth Accounting and the Valuation of Ecosystem Services (WAVES) Global Partnership, led by the World Bank. WAVES extended and institutionalized the accounting work, updating Central Framework accounts, developing ecosystem accounts under the SEEA-EA standard, and piloting an environmental-agriculture account, making Guatemala the first WAVES country to do so (World Bank, 2019 ). Little additional work emerged during the period following the WAVES program, including the publication of the watershed-scale ecosystem service accounts (Pinillos et al., 2025 ). In the third phase, the most tangible result and indicator of sustained institutionalization emerged. The Bank of Guatemala formally incorporated environmental-economic accounting into its Institutional Strategic Plan 2022–2026 (PEI 2022–2026), making it one of the few central banks in Latin America to include natural capital accounting as an explicit strategic objective (Banguat, 2022 ). This commitment led to the creation of a dedicated Environmental Accounting Unit within the Departamento de Estadisticas Macroeconómicas, staffed by a multidisciplinary team of approximately twelve professionals from economics, ecology, geography, and statistics. As a result of this institutional consolidation, Guatemala now reports active preparation of a comprehensive set of accounts covering both SEEA Central Framework and SEEA Ecosystem Accounting modules, a trajectory whose governance implications are discussed later in the paper. Table 1 presents the structure of the System of Environmental-Economic Accounting (SEEA) in Guatemala, linking accounting components to thematic accounts. It shows how assets, flows, expenditures, and aggregates are distributed across different environmental areas such as forests, water, energy, and land. The table highlights that natural resources and products are widely covered across multiple accounts, reflecting their central role in environmental-economic interactions. Ecosystem-related elements appear mainly in land and ecosystem accounts, indicating a more specialized treatment. Flows such as residues and ecosystem inputs demonstrate how environmental pressures and services are recorded within the system. Expenditures on environmental protection and resource management are consistently included, showing the importance of tracking policy responses. Finally, aggregates like depletion and complementary indicators provide summary measures for analysis. Overall, the table illustrates the SEEA framework's integrated and modular nature, supporting comprehensive environmental accounting and informed decision-making. Table 1 Matrix of the SEEA Accounting Framework Structure in Guatemala Accounting structure of SEEA (by account) CB CRH CRS CEE CTE CRPA CR CGTA Assets Natural resources ☑ ✓ ☑ ☑ ☑ Ecosystems ✓ ☑ Land and surface water ✓ ✓ ✓ ☑ Flows Natural resources ✓ ✓ ✓ ✓ ☑ Ecosystem inputs ✓ ✓ ☑ Products ☑ ☑ ☑ ☑ ☑ ✓ ☑ Residues ✓ ✓ ✓ ✓ ✓ ☑ Expenditures and transactions Environmental protection * * * * * * * * Resource management * * * * * * Aggregates Depletion * * Degradation Complementary indicators ☑ ☑ ☑ ☑ ☑ ☑ ☑ * Note. Symbols indicate the presence or relevance of each element within the account: ☑ (main inclusion), ✓ (partial inclusion), * (applicable/recorded). Abbreviations: CB = Forest Account; CRH = Water Resources Account; CRS = Subsoil Resources Account; CEE = Energy and Emissions Account; CTE = Land and Ecosystems Account; CRPA = Environmental Protection Expenditure Account; CR = Residues Account; CGTA = Environmental Transactions Account. Adapted from INE (Guatemala). Table 2 identifies the main national institutions responsible for providing data to support the System of Environmental-Economic Accounting (SEEA) in Guatemala. The table shows that environmental information is generated through a decentralized network of specialized institutions, each contributing according to its sectoral mandate. For example, forest data are primarily produced by forestry and conservation agencies, whereas water-related information is produced by environmental authorities, statistical offices, and hydrometeorological institutions. Energy and subsoil accounts rely heavily on the Ministry of Energy and Mines, complemented by market and environmental entities. Agricultural institutions play a key role in the collection of land, ecosystem, and fisheries data. Meanwhile, waste and emissions data are largely managed at the municipal level, reflecting local responsibilities. Financial and policy-related information is provided by fiscal and planning institutions. Overall, the table highlights the interinstitutional coordination required to compile environmental accounts and underscores the importance of integrating diverse data sources for comprehensive environmental-economic analysis. Table 2 Main National Institutions Providing Information to the SEEA in Guatemala Topic Main information sources Forest National Forest Institute (INAB); National Council of Protected Areas (CONAP); National Institute of Statistics (INE) Water Ministry of Environment and Natural Resources (MARN); National Institute of Statistics (INE); National Institute of Seismology, Volcanology, Meteorology and Hydrology (INSIVUMEH) Subsoil Ministry of Energy and Mines (MEM) Energy Ministry of Energy and Mines (MEM); Wholesale Market Administrator (AMM); National Institute of Statistics (INE); Ministry of Environment and Natural Resources (MARN) Land and ecosystems Ministry of Agriculture, Livestock and Food (MAGA); National Institute of Statistics (INE) Fisheries resources Fisheries and Aquaculture Unit of MAGA (UNIPESCA); Ministry of Agriculture, Livestock and Food (MAGA) Waste and emissions Municipal Development Institute (INFOM); Municipality of Guatemala Expenditures and transactions Ministry of Public Finance (MINFIN); Tax Administration System (SAT); Municipal Development Institute (INFOM); Secretariat of Planning and Programming of the Presidency (SEGEPLAN) Note. Adapted from INE (Guatemala). Institutional acronyms are preserved in Spanish. 4. Results The results are organized in two parts reflecting Guatemala's two-tier accounting program. Section 4.1 presents key findings from the SEEA Central Framework accounts, summarizing what two decades of asset-by-asset accounting have revealed about natural capital depletion across forests, water, energy, and related sectors. Sections 4.2 to 4.6 then present the SEEA-EA ecosystem accounts, covering ecosystem extent, changes over time, ecosystem condition, ecosystem service supply, and the economic relevance of those services. 4.1. SEEA Central Framework accounts Guatemala's SEEA Central Framework accounts, compiled for the period 2001–2010 and updated through 2019, provide the most comprehensive empirical record of natural capital dynamics available for any Central American economy (Table 3 ). The results reveal a consistent pattern: the country's economic growth was systematically sustained by the drawdown of natural capital stocks that did not appear in conventional national accounts (IARNA-URL, 2012). The forest accounts document that Guatemala lost approximately 40% of its forest cover between 1970 and 2005, with an annual deforestation rate of 1.7%, roughly three times the regional average for Latin America and the Caribbean. Over 95% of timber extraction occurred outside formal institutional control, indicating that the legal economy captured only a fraction of actual forest use (Banguat and IARNA-URL, 2009). The real contribution of forests to the national economy was estimated at 3.15% of GDP in 2001 and 2.57% in 2006, compared with approximately 1% recorded in conventional national accounts, a systematic undervaluation of more than threefold. The cumulative economic cost of forest degradation through erosion loss and carbon stock depletion reached approximately USD 374 million between 1991 and 2003 (INE, Banguat, and IARNA-URL, 2013; Castañeda et al., 2019 ). The water accounts documented the cost of water-related diseases from contaminated sources at 0.97% of GDP annually. The flow accounts further revealed that the agricultural sector consumed 53% of national water while contributing only 12% of GDP, a structural dependency ratio that makes visible the hidden role of natural capital in economic performance (Banguat and IARNA-URL, 2009). The energy accounts reveal that biomass (primarily firewood) remains the dominant household energy source in rural Guatemala, with total greenhouse gas emissions from energy combustion reaching 45.6 million tonnes of CO2 equivalent over the accounting period (Castañeda et al., 2014 ). Natural capital represented 23.1% of total national wealth in 1995, declining to 16.3% by 2020, more than double the high-income country average of approximately 8% (INE, Banguat and IARNA-URL, 2013). At the sub-national level, the earliest SCAEI series (2001–2006) estimated the combined value of forest and subsoil assets at approximately GTQ 666 billion by 2006, nearly twice the value of produced capital in the same year, underscoring the structural weight of natural assets in Guatemala's national balance sheet (Banguat and IARNA-URL, 2009). The Adjusted Net Savings (ANS) indicator, which corrects GDP-based savings for natural capital depletion and pollution damage, averaged just 1.6% of GNI during 1995–2016 and turned negative in several years, a signal that Guatemala was, in aggregate, consuming its asset base rather than building it (Castañeda et al., 2019 ; World Bank, 2024 ). Table 3 summarizes key quantitative results across the Central Framework account domains. Table 3 Key Quantitative Results from Guatemala's Environmental-Economic Accounts Indicator Quantitative result Period Forest accounts Forest cover loss 40% of the total forest cover 1970–2005 Annual deforestation rate 1.7% per year (3× the LAC regional average) 2001–2010 Timber extraction without institutional oversight Over 95% of total volume extracted 2001–2010 Real contribution of forests to GDP 3.15% (2001) and 2.57% (2006) vs. ~1% in conventional accounts 2001–2006 Economic cost of forest degradation (erosion and carbon) Q2,919.4 million (~ USD 374.3 million) accumulated 1991–2003 Water and agriculture accounts Main water demand sectors Maize (rain-fed) and sugarcane (irrigated); high climate variability dependence 2001–2013 Sugarcane share of total crop volume 70% of total agricultural production 2001–2013 Crop contribution to GDP 9% of GDP; other agricultural products, 2% additional 2001–2013 Energy accounts Primary energy source in rural households Firewood (biomass), with negative impact on forest cover 2001–2013 Total GHG emissions from energy combustion 45.6 million tonnes CO₂ equivalent 2001–2013 Energy intensity of electricity sector 14.2 TJ per million GTQ of GDP 2001–2013 Land and ecosystem accounts (Central Framework) Ecoregions with critical fragmentation 9 of 14 ecoregions with compromised ecological integrity 2003 Land degradation and risk 10% of territory highly degraded; 63% at high degradation risk 2010 Annual cost of soil degradation 0.55% of GDP per year 2010 Cost of waterborne diseases from contaminated sources 0.97% of GDP per year 2010 Fisheries accounts Fisheries and aquaculture contribution to GDP 0.19%–0.25% of GDP; over 50% of production exported 2001–2005 Employment in fisheries and aquaculture 14,133 persons directly employed 2001–2013 Wealth and Adjusted Net Savings Natural capital share of total wealth 21% of total wealth (more than double the global average of ~ 9%) 2014 Natural capital trend Decline from 23% to 21% of total wealth 1995–2014 Adjusted Net Savings (ANS) average 1.6% of GNI; several years with negative values 1995–2016 Note. GTQ = Guatemalan quetzal; TJ = terajoules; ANS = Adjusted Net Savings; LAC = Latin America and Caribbean; GHG = greenhouse gas. GDP and GNI in constant prices where available. Sources: Banguat and IARNA-URL (2009); INE, Banguat and IARNA-URL (2013); Castañeda et al. ( 2019 ); World Bank ( 2019 , 2024 ). 4.2. Ecosystem extent The accounts adopt Holdridge life zones as the unit of analysis for ecosystem extent, treating each of the 13 bioclimatic zones identified for Guatemala as a distinct ecosystem type. This choice follows the rationale that life zones, delimited by combinations of mean annual biotemperature, precipitation, and potential evapotranspiration, capture the floristic physiognomy and structural characteristics that differentiate ecosystems across Guatemala's highly variable altitudinal and precipitation gradient (IARNA-URL, 2018 , as cited in Banco Mundial et al., 2021). The system has an empirical and objective basis and is widely recognized as appropriate for tropical and subtropical vegetation classification, offering greater ecological resolution than administratively derived zoning systems (Lugo et al., 1999 ). Its main strengths for ecosystem mapping lie in its grounding in the climatic drivers of ecosystem processes, recognition of plants' ecophysiological responses, and hierarchical use of additional mapping criteria at finer levels of ecological complexity. The system also offers two operational advantages that are particularly relevant for a national accounting exercise: the classifications can be georeferenced, enabling spatially differentiated analysis and policy recommendations, and they can be updated dynamically as new climatic data become available (Banco Mundial et al., 2021). Because life zones integrate the climatic drivers that determine vegetation structure and composition, their spatial extent serves as a meaningful proxy for the territorial extent of each ecosystem type. In Guatemala, the humid tropical forest (bh-T) is the most extensive ecosystem, covering 3.43 million hectares and representing 31.7% of national territory. It is followed by the dry tropical forest (bs-T) at 2.08 million hectares (19.2%). Together with the premontane humid forest (bh-PMT) and the lower montane humid forest (bh-MBT), these four life zones account for nearly 77% of the country's total area. Approximately 6.47 million hectares (roughly 60%) of national territory supports natural ecosystems, while 4.29 million hectares (approximately 40%) are classified as artificial ecosystems, including croplands, pastures, and urban areas. Full extent data by life zone are presented in Table 4 . Table 4 Ecosystem Extent by Holdridge Life Zone, Guatemala (2012) Moisture province Life zone Area (ha) % of country area Very dry Bosque muy seco tropical (bms-T) 81,887 0.76% Dry Bosque seco tropical (bs-T) 432,226 4.00% Bosque seco premontano tropical (bs-PMT) 147,803 1.37% Humid Bosque húmedo tropical (bh-T) 3,427,543 31.72% Bosque húmedo premontano tropical (bh-PMT) 704,248 6.52% Bosque húmedo montano bajo tropical (bh-MBT) 338,283 3.13% Very humid Bosque muy húmedo tropical (bmh-T) 963,265 8.91% Bosque muy húmedo premontano tropical (bmh-PMT) 648,133 6.00% Bosque muy húmedo montano bajo tropical (bmh-MBT) 273,898 2.53% Bosque muy húmedo montano sub-andino tropical (bmh-ST) 170,516 1.58% Pluvial Bosque pluvial premontano tropical (bp-PMT) 127,800 1.18% Bosque pluvial tropical (bp-T) 86,413 0.80% Bosque pluvial sub-andino tropical (bp-SAT) 41,010 0.38% Total 10,820,725 100% Note. Life zone abbreviations: bms = bosque muy seco; bs = bosque seco; bh = bosque húmedo; bmh = bosque muy húmedo; bp = bosque pluvial; T = tropical; PMT = premontane tropical; MBT = lower montane tropical; MT = montane tropical; SAT = subandean tropical. Source: IARNA-URL ( 2018 ); Banco Mundial et al. (2021, Cuadro 1). Forest cover dynamics in the 1991–2001 period, when Guatemala's annual deforestation rate was estimated at approximately 1.43% of forest cover per year, represented the most acute phase of forest loss. Subsequent periods showed some moderation in annual net loss rates, associated in part with the consolidation of the National System of Protected Areas (SIGAP) and the expansion of forest incentive programs. The 2010–2014 period shows continued but lower annual losses, with net deforestation estimated at 17,000–19,000 hectares per year nationally. The persistence of high household fuelwood dependence throughout this period was a primary driver of continued pressure on forest cover (van Kempen et al., 2009 ). Net forest loss over the full 1991–2014 period reached approximately 1.07 million hectares, distributed unevenly across life zones as shown in Table 5 . Table 5 Net Forest Loss by Life Zone and Sub-period, Guatemala (1991–2014) Life zone 1991–2001 2001–2006 2006–2010 2010–2014 Net (ha) Ann. (ha) Net (ha) Ann. (ha) Net (ha) Ann. (ha) Net (ha) Ann. (ha) bms-T -887 -100 -617 -104 224 59 -70 -17 bs-T -37,732 -4,249 -50,025 -8,422 -44,571 -11,668 -20,992 -5,248 bs-PMT -15,447 -1,740 -10,936 -1,841 -11,372 -2,977 -270 -67 bh-T -307,731 -34,654 -236,069 -39,742 -80,720 -21,131 -92,747 -23,187 bh-PMT -93,183 -10,494 -21,341 -3,593 -30,717 -8,041 -32,189 -8,047 bh-MBT -25,821 -2,908 17,452 2,938 18,977 4,968 35,687 8,922 bmh-T -27,985 -3,152 -7,871 -1,325 2,949 772 13,063 3,266 bmh-PMT -39,477 -4,446 6,691 1,126 -8,626 -2,258 4,877 1,219 bmh-MBT -5,305 -597 10,837 1,824 3,186 834 4,214 1,054 bmh-MT -6,939 -781 4,801 808 3,220 843 11,718 2,930 bp-PMT -1,415 -159 1,453 245 1,186 311 1,491 373 bp-MT 8 1 25 4 -5 -1 -97 -24 bp-SAT -176 -20 44 7 58 15 -37 -9 Total -562,089 -63,298 -285,554 -48,073 -146,212 -38,275 -75,350 -18,838 Note. Negative values indicate net forest loss (ha); positive values indicate net gain. Ann. = annual net change (period total ÷ years). Sources: Banco Mundial et al. (2021, Cuadros 5.1–5.4); UVG, INAB, CONAP & URL (2006, 2011, 2012); GIMBUT ( 2018 ). Life zone abbreviations: bms = bosque muy seco; bs = bosque seco; bh = bosque húmedo; bmh = bosque muy húmedo; bp = bosque pluvial; T = tropical; PMT = premontane tropical; MBT = lower montane tropical; MT = montane tropical; SAT = subandean tropical. Source: IARNA-URL ( 2018 ); Banco Mundial et al. (2021, Cuadro 1). 4.3. Ecosystem extent changes over time Forest cover dynamics within each Holdridge life zone track how ecosystem extent has changed over time, providing the temporal dimension of the extent accounts. Table 5 presents net forest loss by life zone and sub-period from 1991 to 2014. The data draw on five national forest cover maps produced by the Interinstitutional Forest and Land Use Monitoring Group (GIMBUT) and organized by the same 13 life zone units used in Table 4 . The sub-period structure allows identification of trends in deforestation pressure and recovery that a single-point comparison would obscure. 4.4. Ecosystem condition Forest cover dynamics within each life zone are used as the primary proxy for ecosystem condition. This approach is grounded in the recognition that forest cover stability is strongly indicative of an ecosystem's broader functional integrity. Research consistently shows that deforestation disrupts the soil, water, and biogeochemical cycles that underpin ecosystem service provision: deforestation of tropical forest profoundly impacts soil properties and functions, including nutrient storage and recycling, carbon storage and greenhouse gas emissions, erosion resistance, and water storage, drainage, and filtration, with changes continuing for decades after forest clearing and eventually extending to deep subsoils (van Straaten et al., 2015 ). At the landscape scale, forest patches embedded within deforested landscapes experience significant alterations in nutrient cycling and carbon stocks, reductions in frugivory and seed dispersal, and a simplification of ecological interactions, with forest specialist species exhibiting reduced diversity (Leal et al., 2023 ). Within ecosystem accounting frameworks, tree cover density has been validated as a core structural indicator of forest condition under the SEEA Ecosystem Condition Typology (Czúcz et al., 2021 ; Maes et al., 2023 ), and forest degradation has multiple negative consequences, reducing economic output, slashing the capacity of forests to deliver ecosystem services such as flood protection and nature-based recreation, and contributing to biodiversity loss. From an accounting perspective, the availability of multi-date national forest cover maps produced by the Interinstitutional Forest and Land Use Monitoring Group (Gimbut) for 1991, 2001, 2006, 2010, and 2014 makes forest cover the most temporally consistent and spatially resolved condition indicator accessible at the ecosystem level in Guatemala. While the Guatemalan ecosystem accounts acknowledges that ideal condition assessment would additionally incorporate indicators such as species richness, soil erosion rates, and water availability, data constraints rendered most of these inviable at the national scale for the reference period; forest cover dynamics therefore serve as the best available approximation of ecosystem condition given the current state of national information systems (Banco Mundial et al., 2021). The Guatemala Ecosystem Account (Cuenta de Ecosistemas de Guatemala [EA]; Banco Mundial et al., 2021) assesses ecosystem condition through several indicators derived from forest cover and protected-area dynamics. The present section reports a subset of two, those for which sufficient spatial and temporal data were available and that are most directly relevant to the analysis developed in this paper: (1) the Normalized Difference Vegetation Index (NDVI), (2) isolation and external pressure on legally protected areas. NDVI as a Proxy for Ecological Integrity The Normalized Difference Vegetation Index (NDVI) provides a proxy for the ecological integrity of forested areas, capturing vegetation health and density through the relationship between near-infrared reflectance—which is high in healthy plant tissue—and visible red reflectance, absorbed by chlorophyll during photosynthesis (El-Gammal et al., 2014, as cited in Banco Mundial et al., 2021). Index values range from − 1 to 1: values below 0.2 correspond to open water or bare ground; 0.2–0.4 to sparse vegetation; 0.4–0.6 to moderately dense vegetation; and values above 0.6 to dense, structurally complex vegetation (Al-doski et al., 2013, as cited in Banco Mundial et al., 2021). For the EA, NDVI was calculated from Landsat 8 imagery (30 m resolution) acquired between 2017 and 2019 (United States Geological Survey [USGS], 2020). The upper range (> 0.6) was further subdivided to improve discrimination among forest conditions, yielding the six-class scale presented in Table 6 . The densest and healthiest forests (NDVI > 0.8) were found in the Sierra de las Minas mountain range, the Sierra de los Cuchumatanes, and in humid tropical forest areas within the Verapaces region. These were followed by dry tropical forest (bs-T), which also exhibited relatively high NDVI values despite lower annual precipitation. In contrast, forests north of the volcanic cordillera showed the lowest vegetation density values. Table 6 summarises the dominant NDVI ranges by ecosystem. Table 6 Dominant NDVI Range and Vegetation Condition by Ecosystem Type (2017–2019) Code Ecosystem (Holdridge zone) Humidity province Dominant NDVI range Vegetation condition bms-T Bosque muy seco tropical Very dry 0.4–0.6 Moderate bs-T Bosque seco tropical Dry 0.6–0.8 Dense (among highest) bs-PMT Bosque seco premontano tropical Dry 0.4–0.6 Moderate bh-T Bosque húmedo tropical Humid 0.6–0.8 Dense (esp. Verapaces) bh-PMT Bosque húmedo premontano tropical Humid 0.4–0.6 Moderate bh-MBT Bosque húmedo montano bajo tropical Humid 0.6–0.8 Dense (Sierra de las Minas) bmh-T Bosque muy húmedo tropical Very humid 0.6–0.8 Dense bmh-PMT Bosque muy húmedo premontano tropical Very humid 0.4–0.6 Moderate bmh-MBT Bosque muy húmedo montano bajo tropical Very humid 0.6–0.8 Dense bmh-MT Bosque muy húmedo montano tropical Very humid 0.6–0.8 Dense (Sierra de los Cuchumatanes) bp-PMT Bosque pluvial premontano tropical Pluvial 0.6–0.8 Dense bp-MT Bosque pluvial montano tropical Pluvial 0.6–0.8 Dense bp-SAT Bosque pluvial subandino tropical Pluvial 0.2–0.4 Sparse (rocky/open) Note. Based on Banco Mundial et al. (2021), using USGS Landsat 8 imagery (2017–2019). NDVI values represent approximate dominant ranges derived from the spatial distribution analysis reported in the EA. Values for individual protected areas or localities may differ. Forest vegetation density was classified using the NDVI following six categories: values above 0.85 indicate very dense, structurally complex intact forest; values between 0.80 and 0.85 correspond to healthy, very dense forest with high biomass and minimal stress; values between 0.60 and 0.80 reflect dense forest with continuous canopy cover; values between 0.40 and 0.60 indicate moderate, fragmented or disturbed forest; values between 0.20 and 0.40 represent sparse vegetation such as shrubland or degraded areas; and values below 0.20 correspond to areas with no vegetation, including bare soil, water bodies, or urban surfaces (adapted from Banco Mundial et al., 2021, Table 8 , based on Al-doski et al., 2013). External Pressure and Isolation of Legally Protected Areas (2001–2014) In addition to internal deforestation, the EA assessed the extent to which protected areas are under pressure from surrounding land-use change and are becoming progressively isolated from other natural areas. Following established conservation planning practice, pressure was operationalized using deforestation recorded in a 1 km buffer around each protected area (proxying immediate edge pressure), while isolation was assessed through forest loss in a 10 km buffer (proxying landscape-scale connectivity; Banco Mundial et al., 2021). The analysis focused on 37 protected areas belonging to the three most restrictive management categories (I, II, and VI). Systematic deforestation in both buffer widths was recorded for all areas assessed, indicating a trend of progressive isolation that compromises the viability of large-mammal populations and reduces the provision of ecosystem services, including hydrological regulation, carbon sequestration, and cultural services. Table 7 summarises the qualitative isolation and pressure profile for a selection of these protected areas. Table 7 Isolation and External Pressure on Selected Legally Protected Areas (2001–2014) Protected area (ecosystem) Size class Pressure (1 km buffer) Isolation risk (10 km buffer) Overall level Laguna del Tigre NP (bs-T / bmh-T) Large High High Severe Sierra del Lacandón NP (bs-T / bmh-T) Large High High Severe Mirador-Río Azul NP (bs-T / bmh-T) Large Moderate Moderate Moderate Maya Biosphere Reserve (overall) Very large High High Severe Sierra de las Minas BR (bmh-MBT / bh-T) Medium Moderate Low–moderate Moderate Atitlán Volcano NP (bmh-PMT) Small High High Critical Trifinio (bmh-MBT) Medium Moderate Moderate Moderate Note. Pressure reflects deforestation intensity in the 1 km buffer zone; isolation risk reflects deforestation intensity in the 10 km buffer zone. Qualitative levels (low/moderate/high/severe) are derived from the relative deforestation rates reported in Banco Mundial et al. (2021). NP = National Park; BR = Biosphere Reserve. The Maya Biosphere Reserve and its connecting biological corridors face particularly severe pressure: losses in the four connecting corridors accounted for 84% of the total forest area that disappeared at the national level between 2001 and 2014, illustrating how progressive isolation amplifies the ecological risks already identified at the individual protected area level (Banco Mundial et al., 2021). Taken together, the indicators reported in this section reveal a consistent pattern of ecosystem degradation across Guatemala: vegetation quality remains highest in mountain ranges and remote humid areas; formal protection does not guarantee the preservation of forest cover; protected areas are becoming increasingly isolated due to surrounding deforestation; and forest loss outside protected areas is most acute in the ecosystems that already experienced the greatest historical pressures. These findings underscore the need for complementary conservation instruments—including landscape-level planning, biological corridor management, and land-use policies for unprotected forestlands—to sustain the ecosystem services documented in the EA. 4.5. Ecosystem services supply The SEEA-EA ecosystem accounts draw on a protected area inventory covering 189 of Guatemala’s 334 SIGAP areas and representing 95% of total protected area extent. Regulating services were the most frequently reported category overall, with regulation of hydrological flow and the water cycle accounting for 122 reports across all management categories. This reflects the critical role of protected forests in maintaining watershed function across the country's 38 major river basins (Pinillos et al., 2025 ). Soil erosion control (59 reports) and regulation of extreme events (43 reports) were the next most-cited regulatory services. Cultural services were the most reported category by number of records when Type V (protected landscapes) areas are included. Biodiversity conservation was the single most frequently documented service across the full dataset (183 records), followed by recreation and ecotourism (85 records), cultural and spiritual significance for Indigenous communities (64 records), and research and education services (62 records). Table 8 presents ecosystem service reports by SIGAP management category across 189 of 334 assessed protected areas, representing 95% of protected area extent. Table 8 Ecosystem Services Reported in Protected Areas by SIGAP Management Category Category Protection type Ecosystem service I II III IV V VI Total Provisioning Cultivated plants & animals 7 1 4 13 4 4 33 Wild plants for food 1 2 1 0 2 3 9 Rearing animals 0 2 0 2 2 1 7 Wild animals for food 4 1 3 4 2 3 17 Aquatic transport 1 1 0 1 0 1 4 Water 12 7 3 11 6 9 48 Timber 1 0 0 2 1 3 7 Non-timber forest products 4 0 2 5 2 4 17 Carbon 5 3 2 7 3 6 26 Regulating Regulation of hydrological flows 15 5 5 14 6 10 55 Water purification 10 4 3 9 5 8 39 Soil erosion control 11 4 4 10 5 8 42 Pollination 3 1 2 4 2 4 16 Climate regulation 7 3 2 8 3 7 30 Hazard regulation 5 2 2 6 3 5 23 Pest & disease regulation 4 1 2 5 2 4 18 Cultural Biodiversity conservation 15 5 5 14 6 12 57 Recreation & tourism 10 4 3 10 5 9 41 Spiritual & cultural values 8 3 3 8 4 8 34 Research & education 7 3 2 7 3 7 29 Biophysical characteristics 5 2 2 6 3 6 24 Aesthetic values 4 2 2 5 2 5 20 Total 152 57 52 151 72 136 620 Note. Values = number of service reports. 189 of 334 SIGAP areas assessed, representing 95% of protected area extent and 29% of national territory. Type I = strict reserves; Type II = national parks; Type III = natural monuments; Type IV = habitat/species management; Type V = protected landscapes; Type VI = managed resource areas. Source: Banco Mundial et al. (2021, Cuadro 11.1). 4.6. Economic relevance of ecosystem services The monetary valuation synthesis covers approximately 1.73 million hectares across Guatemala's natural areas. The total estimated annual flow amounts to approximately USD 843 million, equivalent to roughly 3.6% of Guatemala's 2019 GDP. This encompasses 30 specific services across the three SEEA-EA categories and reflects a partial and conservative estimate, as it covers only areas where valuation studies were available. Cultural services represent the largest share of estimated economic value at approximately USD 439 million per year (52% of total), dominated by the composite non-use value of intact natural areas (USD 378 million/year) and tourism and recreation (USD 58 million/year). Regulating services account for approximately USD 294 million per year (35%), led by nutrient cycling (USD 263 million/year) and hydrological regulation (USD 15.5 million/year). Provisioning services account for approximately USD 111 million per year (13%), led by aquatic transport (USD 39.7 million/year) and fisheries (USD 32.6 million/year). The full breakdown is presented in Table 9 . Table 9 Estimated Annual Economic Value of Ecosystem Services in Guatemala Category Ecosystem service Annual value (USD/year) Area (ha) Cultural Biophysical characteristics 377,758,388 47,039 Tourism 56,000,000 600,000 Recreation 5,400,000 50,000 Regulating Carbon sequestration (REDD+) 120,000,000 4,200,000 Hydrological regulation (hydropower) 90,000,000 3,800,000 Water supply regulation 52,000,000 2,100,000 Soil erosion control 32,000,000 950,000 Provisioning Aquatic transport 39,700,000 320,000 Timber and non-timber forest products 38,000,000 480,000 Freshwater supply 33,300,000 290,000 Total All services (partial estimate) 843,158,388 12,837,039 Note. Values in nominal USD. Area = geographic extent of each valuation case study. Cultural services account for 52% of total value; regulating 35%; provisioning 13%. The total (USD 843 million/year) represents approximately 3.6% of Guatemala's 2019 GDP and is a partial and conservative estimate. Sources: Banco Mundial et al. (2021, Cuadro 12.1); IARNA-URL ( 2019 ). 5. Discussion: What Two Decades of Accounts Reveal 5.1 What Does the Data Show Guatemala's two-decade accounting record yields a convergent empirical finding: the country's economy is structurally more dependent on natural capital than conventional statistics suggest, and that capital is being depleted faster than it is being replenished. Forest contributions to GDP were estimated at more than three times the value recorded in conventional national accounts; water services sustain agriculture and energy production in ways invisible to GDP; and ecosystem services generate an estimated USD 843 million per year in economic value that never enters the ledger (Castañeda et al., 2019 ; Banco Mundial et al., 2021). These findings directly substantiate the ecological economics argument that economic systems are embedded within, and ultimately constrained by, the ecological systems that sustain them (Costanza et al., 2014 ; Daly, 1996 ). But the program also reveals something equally important: making this dependency visible requires sustained institutional investment. The patterns documented here only became legible after more than twenty years of continuous accounting effort, and their policy relevance depended on how the program was designed and embedded in governance structures from the outset. The conditions account for land-use change as the primary driver of ecosystem transformation. Agricultural expansion, infrastructure development, and population pressure have converted natural ecosystems at rates consistent with broader tropical deforestation trends. The resulting fragmentation reduces ecological connectivity and degrades the processes that sustain biodiversity and ecosystem services. Condition indicators show that ecosystems in areas of intensive land conversion exhibit substantially lower ecological integrity than those in remote or formally protected regions, underscoring the value of integrated spatial planning. The valuation synthesis reinforces a point central to ecological economics: the most economically significant ecosystem services are precisely those that markets fail to price. Watershed forests regulate flows sustaining hydropower, irrigation, and urban supply. Vegetation retains soils and maintains agricultural productivity. Intact habitats generate existence values that exceed those of any extractive use. All of these benefits remain invisible in GDP (Brander et al., 2024 ; Freeman et al., 2014 ). Embedding these services in a national accounting framework does not resolve the political economy of land use, but it makes the cost of ignoring them legible to policymakers. 5.2. Contributions to the global development of SEEA Guatemala's experience makes a distinctive contribution to the international literature on environmental-economic accounting. The WAVES Global Partnership, which Guatemala joined as a core implementing country in March 2014, documented across its eight partner countries that sustained country-level technical assistance combined with communities of practice is a necessary condition for successful institutionalization. The process must also be driven by planning or finance agencies rather than treated as a purely statistical exercise (WAVES, 2014 ; Ruijs et al., 2019 ). The GPS-WAVES Annual Report 2019, which synthesized the conclusions of the WAVES program as it transitioned to the Global Program on Sustainability (GPS), confirmed this lesson at scale: across all core implementing countries, programs that achieved durable policy uptake shared three features in common. They all had government ownership at the highest level, alignment with a national development or planning framework, and continuity of technical support across political transitions (WAVES and World Bank, 2020 ). Guatemala's program exemplifies all three. Accounts were produced within INE and Banguat as official national statistics. Accounting priorities were aligned with the K'atun 2032 national development plan and the National Climate Change Action Plan. The IARNA-URL partnership provided the continuity that government institutions alone could not sustain across election cycles (Castañeda et al., 2019 ; World Bank, 2019 ). The three successive NCA Policy Forums organized by WAVES (2016, 2017, 2018) together built a cumulative body of cross-country evidence on what makes accounts useful for governance. The 1st Policy Forum established, drawing on the experience of twelve countries, that NCA is most useful when it covers the full policy cycle of problem identification, policy design, implementation, and monitoring. The Forum also concluded that active engagement between account producers and policy users is a prerequisite for uptake, and that major policy trends including the SDGs and green growth strategies urgently need better information on natural capital (Vardon et al., 2017 ). The 2nd Policy Forum focused specifically on how NCA can support the Sustainable Development Goals, finding that countries with longer NCA experience used accounts most effectively because they had developed multi-disciplinary technical working groups and multi-agency NCA-policy steering committees. This is precisely the governance architecture Guatemala built through its public-academic partnership. Guatemala's forest and water accounts were cited at the 2nd Forum as a case study in which NCA had been used to inform policy on disaster risk, watershed governance, and food security simultaneously (Ruijs and Vardon, 2018 ). The 3rd Policy Forum, focused on climate and biodiversity, reinforced that accounts must be positioned as analytical bridges linking environmental data to the economic decisions of finance and planning ministries, a framing that Guatemala's program had adopted from its earliest phases (Vardon, Bass and Ahlroth, 2019 ). The GPS Annual Report 2020–2021, the first comprehensive report of the program that replaced WAVES, documented that the transition from WAVES to GPS was motivated by lessons learned across the partnership: effective institutionalization requires not only account production but also sustained demand-side uptake, capacity building, and integration with financial decision-making (World Bank, 2021 ). The report confirmed that 84 percent of WAVES Plus indicators had been met or exceeded at the time of transition, but that the remaining gap was concentrated in the more complex governance integration objectives, reflecting the inherent difficulty of moving from account production to policy embedding. Guatemala's program, having operated for more than a decade before WAVES began, had a head start on this integration challenge that most partner countries lacked, and the GPS-WAVES Annual Report 2019 specifically highlighted Guatemala's trajectory as a model for countries entering the GPS program at earlier stages of institutional development (WAVES and World Bank, 2020 ). The regional context amplifies Guatemala's distinctiveness. CEPAL's 2023 survey of environmental statistics across Latin America and the Caribbean, the most comprehensive regional assessment to date, found that while progress in environmental accounting has strengthened across the region over nearly 25 years of monitoring, persistent challenges in dedicated human and financial resources continue to constrain institutionalization in most countries (Alcantar Lopez et al., 2025 ). The survey confirmed that only a small group of countries maintain sustained and institutionalized NCA programs; the majority of LAC countries, particularly in the Caribbean and Central America, have not yet implemented the SEEA framework. The principal barriers identified across the region, which include insufficient dedicated personnel, weak inter-institutional coordination, and limited linkage between accounts and decision-making processes, are precisely the conditions that Guatemala's public-academic partnership model was designed to address (Alcantar Lopez et al., 2025 ; Castañeda, Castillo and Matias, 2017 ). Against this regional backdrop, Guatemala's twenty-year record is not merely notable in comparative terms; it is an outlier, representing a depth and continuity of institutional commitment that few countries in the region have approached. The UNSD's 2025 Global Assessment provides a further benchmark. Of the 98 countries worldwide that implement the SEEA, Guatemala is among those that compile both the SEEA Central Framework and SEEA Ecosystem Accounting at Stage III, meaning regular compilation and dissemination of accounts (UNSD, 2026 ). Among Latin American and Caribbean countries, only 6 of 11 implementing countries have reached Stage III. Across all regions, the accounts Guatemala compiles, spanning energy, water, forests, land, fisheries, emissions, and ecosystem extent, condition, and services, place it among the approximately 15 countries globally with the broadest portfolio of SEEA accounts, a distinction that no other developing country in Central America has achieved. This external verification by the global SEEA monitoring system confirms that what this paper documents as a national record is also recognized as exceptional performance by the international statistical community. Guatemala's distinctiveness extends to the marine dimension of natural capital accounting. The Global Ocean Accounts Partnership's 2024 assessment of ocean accounting across Latin America and the Caribbean found that approximately 93% of all published environmental-economic accounting effort in the region has focused on terrestrial ecosystems, and that Guatemala is the only country in the region with a government-driven ocean-related account under the SEEA Central Framework. This is the fisheries account, whose latest version was published in 2019 (GOAP, 2024 ). The report found that while multiple countries have assessed the potential for ocean accounts, knowledge about environmental-economic accounting methodologies ranges from basic to advanced in Latin America and from limited to none in much of the Caribbean, with the number of accounts and technical expertise correlating directly with the level of sustained international support received (GOAP, 2024 ). This finding underscores a broader lesson: the terrestrial ecosystem accounts documented in this paper were themselves enabled by sustained WAVES and GPS support over a decade; extending the same institutional depth to marine and coastal ecosystems, including Guatemala's Pacific and Atlantic coasts, coral reefs, mangroves, and coastal fisheries, represents a significant and tractable next frontier for the country's accounting program. 5.3. Lessons Learned: Four Conditions for Accounts to Influence Governance Synthesizing Guatemala's own trajectory with the cross-country evidence from the WAVES Policy Forums and GPS annual reports, four conditions emerge as necessary for natural capital accounts to influence governance in a developing country context. These are not hypothetical but documented patterns visible in both Guatemala's program and in comparative programs that lacked them. First, institutional anchoring in a stable, politically insulated host is essential. The public-academic partnership anchored by IARNA-URL and embedded within INE and Banguat provided both technical credibility and continuity across multiple political cycles. The WAVES synthesis report for Guatemala noted directly that prior to WAVES, SEEA institutionalization remained fragile precisely because it lacked this dual anchoring. Accounts existed but had no durable institutional home (World Bank, 2019 ). The 1st NCA Policy Forum documented the same pattern comparatively: programs hosted exclusively within government agencies were disproportionately disrupted by political transitions, while those with academic or statistical co-anchoring showed greater continuity (Vardon et al., 2017 ). Without the university as a stable host, Guatemala's program would have been vulnerable to the frequent changes in government priorities that have derailed similar initiatives elsewhere (Castañeda, Castillo and Matias, 2017 ). The most concrete recent evidence of this institutional logic is Banguat's own trajectory. The Bank of Guatemala's Institutional Strategic Plan 2022–2026 formally inscribed environmental-economic accounting as a strategic objective, resulting in the creation of a dedicated Environmental Accounting Unit within its statistics department. This step transformed accounting from a project-dependent activity into a permanent institutional function (Banguat, 2022 ). This transition from project-based implementation to institutionalized production is precisely what the WAVES partnership identified as the frontier that most developing country programs had not yet crossed. Guatemala has now crossed it. The 2025 UNSD Global Assessment confirmed Guatemala's Stage III status, meaning regular compilation and dissemination, a milestone achieved by only 6 of the 11 Latin American and Caribbean countries implementing the SEEA, and by fewer than a third of African implementing countries (UNSD, 2026 ). For a developing country with Guatemala's resource constraints, this ranking reflects not statistical ambition but durable institutional architecture built over two decades. Second, demand-side alignment with live policy questions transforms accounts from statistical products into governance tools. Across the WAVES partnership, accounts produced in statistical isolation rarely influenced decisions; accounts aligned with specific policy entry points such as budget cycles, development plans, climate commitments, and disaster risk frameworks were taken up and used (Vardon et al., 2017 ; Ruijs et al., 2019 ). The 1st Policy Forum was explicit that account production should be synchronized with recurring policy cycles when feasible, and that accounts already in place provide a ready source of information for unanticipated policy processes (Vardon et al., 2017 ). In Guatemala, the explicit alignment between accounting priorities and the K'atun 2032 plan, the National Climate Change Action Plan, and the design of forest incentive programs created the demand-side pull that kept accounts relevant to decision-makers throughout the WAVES phase (Castañeda et al., 2019 ). The WAVES scoping process for Guatemala identified these policy entry points deliberately before accounts were produced, a sequencing lesson the 2nd Policy Forum highlighted as a model for other countries (Ruijs and Vardon, 2018 ). Third, sequencing matters. Building foundational credibility before attempting more complex accounts reduces political risk and sustains institutional momentum. Guatemala's program established a decade of Central Framework accounts before transitioning to the more data-intensive ecosystem accounts under SEEA-EA. This sequencing built the inter-institutional trust and shared data infrastructure on which the WAVES phase depended. The 1st Policy Forum synthesis noted that programs which attempted ecosystem accounts before establishing Central Framework credibility encountered stronger institutional resistance (Vardon et al., 2017 ). Guatemala’s trajectory, from the 2013 compendium through the WAVES ecosystem accounting phase to post-WAVES watershed-scale accounts, illustrates the staged deepening identified as one of the most replicable features of the Guatemala model for countries at earlier stages of NCA development (Pinillos et al., 2025 ; WAVES and World Bank, 2020 ). Fourth, capacity building embedded within account production rather than separated from it creates durable in-house expertise. Each phase of Guatemala's program transferred methodological knowledge to government counterparts in SEGEPLAN, MARN, INAB, and INE, creating expertise that outlasted any individual project cycle. The GPS Annual Report 2020–2021 documented that the most effective WAVES-era programs combined intensive country-level technical assistance with regional communities of practice, and that this combination distinguished programs with durable capacity from those requiring repeated external support (World Bank, 2021 ). This methodological depth is evidenced by the successive phases of work documented in Section 3 (WAVES and World Bank, 2020 ). The institutional externality generated by this embedded approach, including working relationships, shared data infrastructure, and inter-institutional trust, is rarely captured in project evaluations but may represent one of the most durable returns on the investment (Castañeda, Castillo and Matias, 2017 ; Ruijs et al., 2019 ). 5.4. Methodological Limitations and Future Directions Several methodological limitations merit acknowledgment. Environmental data quality and temporal coverage remain constrained in many developing countries, and condition indicators derived from remote sensing carry uncertainties related to spatial resolution and classification consistency. Monetary valuation estimates depend on methodological assumptions that vary substantially across studies, limiting cross-site comparability and precluding interpretation as precise measures of total economic value. The figures reported here are best understood as conservative lower bounds. The WAVES synthesis report for Guatemala similarly cautioned that valuation coverage was partial and that the USD 843 million estimate reflects only services where case studies were available (World Bank, 2019 ; Banco Mundial et al., 2021). Future work should prioritize improving condition indicator systems, harmonizing valuation protocols across the SEEA-EA framework, and developing approaches for integrating ecosystem accounts more directly into investment appraisal and planning decisions, an agenda that the GPS program has identified as central to its next strategic phase (World Bank, 2021 ). A further limitation is the incomplete coverage of marine and coastal natural capital. As documented by GOAP ( 2024 ), Guatemala's fisheries account is the only government-driven ocean-related SEEA account in Latin America and the Caribbean; yet the coastal and marine ecosystems that sustain fisheries, coastal protection, and blue economy activities remain outside the scope of the ecosystem accounts synthesized in this paper. Extending the accounting framework to these assets, building on the existing SEEA-CF fisheries account and the SEEA-EA methodologies developed for terrestrial ecosystems, is a priority identified in both the post-WAVES watershed accounting work (Pinillos et al., 2025 ) and regional assessments of the ocean accounting frontier (GOAP, 2024 ). 6. Policy Implications Guatemala's accounting program demonstrates that natural capital accounts generate governance value through three interconnected channels. They make the economic significance of ecosystems visible to decision-makers who would otherwise rely on GDP-centric statistics. They provide spatially explicit information linking ecosystem condition to specific sectoral risks in agriculture, water, energy, and climate. And they create a shared evidence base that reduces institutional fragmentation in cross-sectoral environmental governance (Castañeda, Castillo, and Matias, 2017 ; Ruijs et al., 2019 ; Vardon et al., 2017 ). Each channel has a direct counterpart in the cross-country evidence from the NCA Policy Forums documented in Section 5.2 : the 1st Forum on institutional embedding and the full policy cycle (Vardon et al., 2017 ); the 2nd Forum on SDG monitoring and cross-sectoral governance (Ruijs & Vardon, 2018 ); and the 3rd Forum on dual-visibility accounts engaging finance and planning ministries (Vardon, Bass and Ahlroth, 2019 ). On land use, the accounts revealed that approximately 40% of national territory has been converted to artificial ecosystems, with agricultural expansion the dominant driver of forest loss across nearly all life zones. Guatemala's forest incentive programs, PINPEP and PROBOSQUE, were designed and evaluated in a context where accounting data provided independent evidence of deforestation trends and their economic costs. The accounts strengthened the evidence base for their continuation and targeting (Castañeda et al., 2019 ). The 1st NCA Policy Forum specifically cited Guatemala's forest accounts as a case where natural capital accounting led to new regulation and the strengthening of forest institutions -- a policy uptake pathway that required the sustained institutional investment documented in this paper (Vardon et al., 2017 ). The GPS-WAVES Annual Report 2019 likewise highlighted Guatemala's forest and land accounts as among the most policy-relevant outputs of the WAVES program in Latin America (WAVES and World Bank, 2020 ). On water and climate, the hydrological condition accounts identified the life zones and watersheds most critical for maintaining the water supply underpinning hydropower, irrigation, and urban consumption. The energy and forest accounts provided independent quantification of greenhouse gas emissions from biomass combustion and carbon stock depletion, contributing evidence to Guatemala's nationally determined contributions and climate finance instruments. This dual visibility -- of environmental risk and natural capital asset value -- is precisely what climate governance requires but conventional statistics cannot provide, positioning countries like Guatemala to translate sustained accounting capacity into climate finance and adaptation investments (Dasgupta, 2021 ; Edens et al., 2022 ). For countries considering investment in natural capital accounting, Guatemala's experience, supported by the broader evidence from the WAVES partnership and GPS program, suggests that governance returns are real but require sustained commitment. They do not materialize from a single accounting exercise (Ruijs et al., 2019 ; World Bank, 2019 ). The GPS Annual Report 2020–2021 confirmed that the transition from WAVES to GPS was itself a response to this lesson, emphasising long-term institutional support over project-cycle interventions (World Bank, 2021 ). The GPS-WAVES Annual Report 2019 documented that by the close of the WAVES program, core implementing countries including Guatemala had begun to serve as mentors and knowledge-sharing hubs for other countries in their regions -- a multiplier effect that only becomes available after the kind of sustained investment Guatemala's program represents (WAVES and World Bank, 2020 ). The evidence from Guatemala demonstrates that governance returns from natural capital accounting accumulate over time, and that the most durable among them may be the institutional capacity, inter-agency relationships, and shared evidence culture that the process of producing accounts generates alongside the accounts themselves. Guatemala’s experience also carries direct implications for the broader Latin American region. As documented in Section 5.2 , CEPAL’s 2023 regional survey confirms that the barriers Guatemala’s public-academic model addressed remain the dominant constraints across most LAC countries (Alcantar Lopez et al., 2025 ). The institutional architecture Guatemala developed is precisely what the GPS program has identified as needed regionally. And as discussed in Section 5.4 , the blue economy dimensions of natural capital accounting remain the most significant unfinished agenda for the region, offering a tractable extension for countries with the institutional infrastructure already in place (GOAP, 2024 ). 7. Conclusion As environmental pressures mount and the inadequacy of GDP-centric measures of progress becomes harder to ignore, the case for sustained investment in environmental-economic accounting grows stronger. This paper has synthesized Guatemala’s full accounting record, covering more than two decades of SEEA Central Framework and SEEA-EA ecosystem accounts, and has drawn out both the empirical findings and the governance lessons that record contains. What the record reveals is that the empirical and the institutional are inseparable: the structural dependency on natural capital documented in Section 4 only became visible because the institutional architecture described in Section 5 sustained the work long enough to generate it, and the governance lessons are only credible because the accounts they rest on are consistent and continuous. Guatemala’s experience demonstrates that environmental-economic accounting is not a one-time exercise but a long-term institutional commitment, and that the returns to that commitment, in terms of both analytical insight and governance relevance, accumulate over time. The accounts make legible what GDP cannot: that the sectors driving Guatemala's economic output -- agriculture, hydropower, forestry, and tourism -- depend on ecosystems experiencing measurable and ongoing degradation. Sustainable development in this context is not an abstract goal but a concrete accounting challenge: the stock of natural capital must be maintained, and its depletion must register in the statistics that guide investment and policy. More broadly, the study contributes to the growing literature on ecosystem accounting in two ways. It provides empirical evidence on how the SEEA-EA framework can be applied in a tropical developing economy characterized by high biodiversity and significant land-use pressures, demonstrating that accounts can be built from available data and can support policy discussions on natural capital management and sustainable development. And it contributes process evidence on what it takes to make accounts count: the institutional architecture, partnership models, sequencing decisions, and alignment with governance priorities that determined whether two decades of accounting effort produced durable policy change. Both contributions matter for the rapidly expanding community of countries now implementing the SEEA-EA framework, and both are only visible through the kind of long-horizon synthesis this paper attempts. Declarations Author Contribution The author is the sole author of this academic synthesis, having played a key coordinating and advisory role across the three institutional phases spanning more than two decades mentioned in the paper. Collaborative contributions to the underlying accounting work are acknowledged in the Acknowledgments section and carefully cited throughout the text. The synthesis, interpretation, and argumentation in this paper are solely the author's own. Editorial assistance and translation from Spanish to English were provided in part using AI; the author bears full responsibility for all intellectual content and conclusions. Acknowledgement This paper reports on more than two decades of collaborative accounting work to which many people and institutions made essential contributions. The author gratefully acknowledges all experts from the different participating agencies, including experts from INE, Banguat, INAB, MARN, and SEGEPLAN. Special recognition is due to IARNA-URL, which introduced and led the initial phases of this work in Guatemala under the guidance of the author of this paper. Financial support from the Dutch Cooperation, the World Bank, and own institutional funding from Banguat is greatly acknowledged. References Alcantar Lopez, G., Malmierca Castano, A., & Perez Quesada, A. (2025). La situacion de las estadisticas, indicadores y cuentas ambientales en America Latina y el Caribe, 2023. 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The cost of natural capital consumption: Accounting for a sustainable world economy. Ecological Economics , 68 (6), 1850–1857. https://doi.org/10.1016/j.ecolecon.2008.09.019 Brander, L. M., de Groot, R., Schägner, J. P., Guisado-Goñi, V., van 't Hoff, V., Solomonides, S., McVittie, A., Eppink, F., Sposato, M., Do, L., Ghermandi, A., Sinclair, M., & Thomas, R. (2024). Economic values for ecosystem services: A global synthesis and way forward. Ecosystem Services , 66 , 101606. https://doi.org/10.1016/j.ecoser.2024.101606 Castañeda, J. P., & Barrios, J. M. (2015). Towards national ecosystem accounting: Informing development policy in Guatemala . Presentation, WAVES/IARNA-URL, November 2015. https://seea.un.org/sites/seea.un.org/files/towards_national_ecosystem_accounting_informing_development_policy_in_guatemala.pdf Castañeda, J. P., & Pinillos, D. (forthcoming). Economía y ecología: Dos caras de una misma moneda. Revista Economía y Ecología. [In review] Castañeda, J. P., Carrera, J., & Rexhepi, D. (2019). Towards natural capital accounting in Guatemala: Synthesis report . World Bank. https://documentos.bancomundial.org/es/publication/documents-reports/documentdetail/332151561104488571 Castañeda, J. P., Castillo, F. C., & Matias, I. (2017). Implementing natural capital accounting in developing countries: Public-academic partnerships and policy uptake in Guatemala. In M. Vardon, S. Bass, A. Ruijs, & S. Ahlroth (Eds.), Better policy through natural capital accounting: Stocktake and ways forward (pp. 103–114). World Bank. https://documents1.worldbank.org/curated/en/904211580129561872/pdf/Forum-on-Natural-Capital-Accounting-for-Better-Policy-Decisions-Taking-Stock-and-Moving-Forward.pdf Castañeda, J. P., Vargas, R., Gálvez, J., & Tuy, H. (2014). Cuentas de energía como instrumento para evaluar eficiencias sectoriales en la región centroamericana (LC/MEX/L.1155). CEPAL. https://www.cepal.org/es/publicaciones/37133-cuentas-energia-como-instrumento-evaluar-eficiencias-sectoriales-la-region Comte, A., Crouzat, E., & Vallecillo, S. (2022). Ecosystem accounting: Past scientific developments and future directions. Ecosystem Services , 55 , 101425. https://doi.org/10.1016/j.ecoser.2022.101425 Costanza, R., d'Arge, R., de Groot, R., Farber, S., Grasso, M., Hannon, B., Limburg, K., Naeem, S., O'Neill, R. V., Paruelo, J., Raskin, R., Sutton, P., & van den Belt, M. (1997). The value of the world's ecosystem services and natural capital. Nature , 387 , 253–260. https://doi.org/10.1038/387253a0 Costanza, R., de Groot, R., Sutton, P., van der Ploeg, S., Anderson, S. J., Kubiszewski, I., Farber, S., & Turner, R. K. (2014). Changes in the global value of ecosystem services. Global Environmental Change , 26 , 152–158. https://doi.org/10.1016/j.gloenvcha.2014.04.002 Czúcz, B., Keith, H., Driver, A., Jackson, B., Nicholson, E., & Maes, J. (2021). A common typology for ecosystem characteristics and ecosystem condition variables. One Ecosystem , 6 , e58218. https://doi.org/10.3897/oneeco.6.e58218 Daly, H. E. (1996). Beyond growth: The economics of sustainable development . Beacon Press. https://www.worldcat.org/title/beyond-growth-the-economics-of-sustainable-development/oclc/32546514 Dasgupta, P. (2021). The economics of biodiversity: The Dasgupta review . Cambridge University Press. https://doi.org/10.17863/CAM.104602 Díaz, S., Pascual, U., Stenseke, M., Martín-López, B., Watson, R. T., Molnár, Z., Hill, R., & Chan, K. M. A. (2018). Assessing nature's contributions to people. Science , 359 (6373), 270–272. https://doi.org/10.1126/science.aap8826 Edens, B., Maes, J., Hein, L., Obst, C., Siikamäki, J., Schenau, S., Javorsek, M., & Alfieri, A. (2022). Establishing the SEEA ecosystem accounting framework as a global standard. Ecosystem Services , 54 , 101413. https://doi.org/10.1016/j.ecoser.2022.101413 Freeman, A. M., Herriges, J. A., & Kling, C. L. (2014). The measurement of environmental and resource values: Theory and methods (3rd ed.). Routledge. https://www.routledge.com/The-Measurement-of-Environmental-and-Resource-Values-Theory-and-Methods/Freeman-Herriges-Kling/p/book/9780415501583 GIMBUT. (2018). Mapa de cobertura forestal y cambios de uso de la tierra en Guatemala . MARN/INAB/CONAP. https://www.inab.gob.gt/documentos-publicaciones/ GOAP. (2024). Status of ocean accounting in Latin America and the Caribbean . Global Ocean Accounts Partnership Secretariat, UNSW. https://oceanaccounts.org/files/EN-Status-of-Ocean-Accounting-in-Latin-America-and-the-Caribbean--2-.pdf Hein, L., Bagstad, K., Edens, B., Obst, C., & de Jong, R. (2020). Defining ecosystem assets for natural capital accounting. Science , 367 (6477), 514–515. https://doi.org/10.1126/science.aaz8901 Holdridge, L. R. (1967). Life zone ecology . Tropical Science Center. https://www.worldcat.org/title/life-zone-ecology/oclc/859576 IARNA-URL (Instituto de Agricultura, Recursos Naturales y Ambiente de la Universidad Rafael Landívar). (2012). Perfil Ambiental de Guatemala 2010–2012: Vulnerabilidad local y creciente construcción de riesgo. Serie Perfil Ambiental No. 12. Universidad Rafael Landívar. https://www.url.edu.gt/publicacionesurl/FileCS.ashx?Id=40177 IARNA-URL. (2018). Cuenta de extensión de ecosistemas de Guatemala: Base de datos geoespacial. IARNA-URL. https://iarna.url.edu.gt/publicaciones/ IARNA-URL. (2019). Base de datos de valoración de servicios ecosistémicos de Guatemala. IARNA-URL. https://iarna.url.edu.gt/publicaciones/ INAB. (2005). Propuesta de corredores biológicos de Guatemala. INAB. https://www.inab.gob.gt/documentos-publicaciones/ INE, Banguat, & IARNA-URL. (2013). Sistema de Contabilidad Ambiental y Económica de Guatemala 2001–2010: Compendio estadístico. INE. https://www.ine.gob.gt/sistema/uploads/2014/03/19/mOxhk8AOT3OMR18sus3z2vubUKgBpskK.pdf Leal, C. G., et al. (2023). The breakdown of ecosystem functionality driven by deforestation in a global biodiversity hotspot. Biological Conservation , 283 , 110105. https://doi.org/10.1016/j.biocon.2023.110105 Lugo, A. E., Brown, S. L., Dodson, R., Smith, T. S., & Shugart, H. H. (1999). The Holdridge life zones of the conterminous United States in relation to ecosystem mapping. Journal of Biogeography , 26 (5), 1025–1038. https://doi.org/10.1046/j.1365-2699.1999.00329.x Maes, J., Bruzón, A. G., Barredo, J. I., et al. (2023). Accounting for forest condition in Europe based on an international statistical standard. Nature Communications , 14 , 3723. https://doi.org/10.1038/s41467-023-39434-0 Millennium Ecosystem Assessment. (2005). Ecosystems and human well-being: Synthesis . Island Press. https://www.millenniumassessment.org/documents/document.356.aspx.pdf Pinillos, D., Reyes, P., Barrera, V., Pineda, P., Goyzueta, J. M., Schulte, R., & Castañeda, J. P. (2025). Economic system impacts and dependencies on water-related ecosystem services: Defining analytical spatial units from an ecosystem accounting perspective in Guatemala. Frontiers in Environmental Science , 13 , 1459828. https://doi.org/10.3389/fenvs.2025.1459828 Ruijs, A., & Vardon, M. (Eds.). (2018). 2nd Policy Forum on natural capital accounting for better decision making: Applications for sustainable development . World Bank WAVES. https://www.wavespartnership.org/en/knowledge-center/2nd-policy-forum-natural-capital-accounting-better-policy-decisions-applications Ruijs, A., Vardon, M., Bass, S., & Ahlroth, S. (2019). Natural capital accounting for better policy. Ambio , 48 , 714–725. https://doi.org/10.1007/s13280-018-1107-y Stiglitz, J. E., Sen, A., & Fitoussi, J.-P. (2009). Report by the Commission on the Measurement of Economic Performance and Social Progress . INSEE. https://www.insee.fr/en/information/2662494 United Nations. (2012). System of environmental-economic accounting: Central framework . United Nations. https://seea.un.org/content/seea-central-framework United Nations. (2021). System of environmental-economic accounting – ecosystem accounting . United Nations Statistics Division. https://seea.un.org/ecosystem-accounting UNSD. (2026). Results of the 2025 Global Assessment of Environmental-Economic Accounting and Supporting Statistics . Background document BG-3f, UN Statistical Commission, 57th session, March 2026. United Nations Statistics Division. https://unstats.un.org/UNSDWebsite/statcom/session_57/documents/BG-3f-Global_Assessment_2025_UNSC-E.pdf United States Geological Survey. (2020). Landsat 8 OLI/TIRS C2 L2 surface reflectance data . U.S. Department of the Interior. https://doi.org/10.5066/P9OGBGM6 UVG, INAB, CONAP, & URL. (2006). Dinámica de la cobertura forestal de Guatemala 1991–2006 y mapa de cobertura forestal 2006 . UVG/INAB/CONAP/URL. https://www.url.edu.gt/ UVG, INAB, CONAP, & URL. (2011). Mapa de cobertura forestal de Guatemala 2010 y dinámica 2006–2010 . UVG/INAB/CONAP/URL. https://www.url.edu.gt/ UVG, INAB, CONAP, & URL. (2012). Mapa de cobertura forestal de Guatemala 2012 y dinámica 2010–2012 . UVG/INAB/CONAP/URL. https://www.url.edu.gt/ van Kempen, L., Muradian, R., Sandóval, C., & Castañeda, J. P. (2009). Too poor to be green consumers? A field experiment on revealed preferences for firewood in rural Guatemala. Ecological Economics , 68 (7), 2160–2167. https://doi.org/10.1016/j.ecolecon.2009.02.007 van Straaten, O., Corre, M. D., Wolf, K., Tchienkoua, M., Cuellar, E., Matthews, R. B., & Veldkamp, E. (2015). Conversion of lowland tropical forests to tree cash crop plantations loses up to one-half of stored soil organic carbon. Proceedings of the National Academy of Sciences , 112 (32), 9956–9960. Vardon, M., Bass, S., & Ahlroth, S. (Eds.). (2019). Natural capital accounting for better policy decisions: Climate change and biodiversity. Proceedings of the 3rd Forum on Natural Capital Accounting for Better Policy Decisions . World Bank WAVES. https://documents1.worldbank.org/curated/en/832031580102680871/pdf/Natural-Capital-Accounting-for-Better-Policy-Decisions-Climate-Change-and-Biodiversity.pdf Vardon, M., Bass, S., Ahlroth, S., & Ruijs, A. (Eds.). (2017). Forum on natural capital accounting for better policy decisions: Taking stock and moving forward . World Bank WAVES. https://documents1.worldbank.org/curated/en/904211580129561872/pdf/Forum-on-Natural-Capital-Accounting-for-Better-Policy-Decisions-Taking-Stock-and-Moving-Forward.pdf Vardon, M., Castañeda, J. P., Nagy, M., & Schenau, S. (2018). How the System of Environmental-Economic Accounting can improve environmental information systems and data quality for decision making. Environmental Science and Policy , 89 , 83–92. https://doi.org/10.1016/j.envsci.2018.07.007 WAVES and World Bank. (2020). GPS-WAVES annual report 2019 . World Bank WAVES Partnership. https://www.wavespartnership.org/en/knowledge-center/gps-waves-annual-report-2019 WAVES. (2014). Annual report 2013–2014: Expansion and growing community of practice on natural capital accounting . World Bank WAVES Partnership. https://www.wavespartnership.org/en/waves-annual-report-highlights-expansion-growing-community-practice-nca World Bank & WAVES Partnership. (2021). Cuenta de ecosistemas de Guatemala . WAVES Partnership. https://www.wavespartnership.org/en/knowledge-center/cuenta-de-ecosistemas-de-guatemala World Bank. (2019). Towards natural capital accounting in Guatemala: Synthesis report . WAVES Partnership. https://seea.un.org/content/towards-natural-capital-accounting-guatemala-synthesis-report World Bank. (2021). Global Program on Sustainability annual report 2020–2021 . World Bank. https://documents1.worldbank.org/curated/en/824441643695834856/pdf/Global-Program-on-Sustainability- Annual-Report-2020-2021.pdf World Bank. (2024). The changing wealth of nations 2024: Revisiting the measurement of comprehensive wealth . World Bank. https://www.worldbank.org/en/publication/the-changing-wealth-of-nations Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9438795","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":624710324,"identity":"695d3c14-c668-40c2-b648-76c577697843","order_by":0,"name":"Juan Pablo Castaneda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACxgYkjgRDBZBkRxPFpUUCQpxhYOBhJqAFYQGIYGwjQgvzjNyDH37U1NXxz24+eOPnPJvE/czMBx/OYLCT08Whj3FGXrJkz7HDEhJ3jiVb9m5LS+xhZks23MCQbGx2AJeWHANpBrYDEgw3cswkeLcdBmrhMZN8wHAgcRtuLca/Gf7VScjfyP8m+XcOcVrMpBnbmCUMbuSwSfM2QLVswKel512aZW/fYcmNN9KMrWWOpRn3HAb6ZYYBbr8YtucevvHjWx2/3I3khzff1NjItrc3H3zYU2Enh1NLAw9WcQPsykFAngG7llEwCkbBKBgFCAAA7bdcNSNl+nIAAAAASUVORK5CYII=","orcid":"","institution":"Universidad del Valle de Guatemala","correspondingAuthor":true,"prefix":"","firstName":"Juan","middleName":"Pablo","lastName":"Castaneda","suffix":""}],"badges":[],"createdAt":"2026-04-16 13:26:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9438795/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9438795/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109249386,"identity":"e737cb59-4cc1-4f3f-a147-ccfb4cc33cbf","added_by":"auto","created_at":"2026-05-14 08:50:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":650507,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9438795/v1/b91d0496-e402-4782-884b-7d3490816cca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"What Two Decades of Environmental-Economic Accounting Reveal About Guatemala’s Natural Capital and Governance","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccelerating ecosystem degradation has intensified scrutiny of prevailing development models. Over the past century, economic growth has driven unprecedented increases in resource extraction, land transformation, and pollution, generating welfare gains while simultaneously contributing to biodiversity loss, climate change, soil degradation, and the erosion of ecosystem services on which human well-being depends (Costanza et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dasgupta, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A fundamental driver of this disconnect is the design of conventional economic indicators, which prioritize short-term production and consumption while leaving the ecological foundations of economic activity unmeasured and, consequently, unmanaged. Closing this gap requires statistical frameworks that treat natural capital as a measurable and depletable asset within national accounting systems.\u003c/p\u003e \u003cp\u003eGross domestic product (GDP) and related indicators measure economic activity through market transactions, providing detailed information on production, investment, and consumption, but omit the contributions of ecosystems and natural resources. Environmental degradation can therefore erode the productive capacity of economies without registering in national accounts (Bartelmus, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Stiglitz et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This measurement gap has stimulated interest in the concept of natural capital, defined as the stock of ecosystems and natural resources that generate flows of goods and services underpinning economic production and human well-being (Costanza et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Millennium Ecosystem Assessment, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Addressing this gap requires moving away from GDP as the sole measure of economic performance toward broader frameworks that integrate natural capital alongside produced and human capital (World Bank, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEcosystem services research has established that nature generates a broad spectrum of economic and social benefits: provisioning services such as food, timber, and freshwater; regulating services including climate regulation, flood mitigation, and water purification; and cultural services encompassing recreation, tourism, and cultural identity (Diaz et al., 2018). Because most of these services flow outside markets, they remain invisible in conventional statistics, creating incentives that systematically undervalue and degrade ecosystems. Ecological economics underscores this problem by framing economic systems as fundamentally embedded in, and constrained by, biophysical limits (Daly, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Integrating ecological data into economic analysis is therefore not merely useful; it is essential for assessing the sustainability of development and for designing policies that safeguard the natural foundations of economic activity.\u003c/p\u003e \u003cp\u003eIn response to these challenges, international statistical organizations have developed frameworks to integrate environmental information into national accounting systems, with the System of Environmental-Economic Accounting (SEEA) being the best-known. The SEEA provides a standardized framework for linking environmental data with the System of National Accounts (SNA) and for compiling accounts for environmental assets such as forests, water resources, and energy, among others (United Nations, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The SEEA Central Framework (SEEA-CF), adopted as an international statistical standard in 2012, establishes the foundational structure for this integration by focusing primarily on individual environmental assets and their interactions with the economy, providing the accounting architecture on which subsequent extensions have been built (United Nations et al., 2014). Building on this framework, the SEEA - Ecosystem Accounting (SEEA-EA) was adopted as an international statistical standard in 2021 (United Nations, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The SEEA-EA framework treats ecosystems as spatial assets that generate flows of ecosystem services over time and organizes ecological information into ecosystem extent accounts, ecosystem condition accounts, and ecosystem services accounts (Edens et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By integrating ecological and economic information within a consistent statistical structure, ecosystem accounting provides a mechanism for incorporating natural capital into economic analysis and policy discussions. Recent research highlights the growing role of ecosystem accounting as a tool for linking environmental sustainability with economic decision-making. Applications of the SEEA-EA framework in several countries demonstrate its potential to support environmental governance, land-use planning, and natural resource management (Comte et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hein et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, empirical applications in tropical developing economies remain relatively limited. This gap is particularly important because many developing countries depend heavily on ecosystem services associated with agriculture, forestry, fisheries, and tourism. Expanding empirical evidence on ecosystem accounting in these contexts can therefore provide valuable insights into the relationship between ecosystems and economic development.\u003c/p\u003e \u003cp\u003eGuatemala is not merely a convenient case study; it is one of the pioneers of environmental-economic accounting among developing nations. The country's accounting program spans more than two decades, beginning in the early 2000s with a public-academic partnership between INE, Banguat, and IARNA-URL, with early support from the Dutch government (INE, Banguat, and IARNA-URL, 2013). This partnership produced SEEA Central Framework accounts covering forests, water, energy, emissions, fisheries, land, and subsoil assets for the period 2001\u0026ndash;2010, compiled as official national statistics and published in a comprehensive statistical compendium in 2013. Building on this foundation, Guatemala formally joined the Wealth Accounting and the Valuation of Ecosystem Services (WAVES) Global Partnership in March 2014, extending and institutionalizing the accounting work through 2019 and adding ecosystem accounts under the emerging SEEA-EA framework (World Bank, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe program entered a third and ongoing phase when Banguat formally incorporated environmental-economic accounting into its Institutional Strategic Plan 2022\u0026ndash;2026, creating a dedicated Environmental Accounting Unit within the Departamento de Estad\u0026iacute;sticas Macroecon\u0026oacute;micas and staffing it with a multidisciplinary team of approximately twelve professionals from economics, ecology, geography, and statistics (Banguat, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This institutional consolidation earned Guatemala recognition as a Stage III SEEA implementer in the 2025 United Nations Statistics Division Global Assessment, placing it among the top tier of developing countries in environmental-economic accounting. The country hosts a wide range of ecosystems, including tropical rainforests, dry forests, wetlands, and montane formations, whose services underpin agriculture, hydropower, forestry, and tourism, making the integration of natural capital into economic planning both ecologically warranted and economically urgent.\u003c/p\u003e \u003cp\u003eDespite this rich institutional history, the results of Guatemala's accounting program have been documented primarily in technical and policy reports rather than in peer-reviewed academic literature. This gap matters: translating national accounting experience into scholarly research strengthens both the theoretical foundations and the practical case for investing in natural capital measurement in similar economies. The present paper addresses this gap by making two interconnected contributions. The first is empirical as it synthesizes two decades of accounting results spanning both the SEEA Central Framework (SEEA-CF), which documents physical and monetary flows across major natural asset categories, and the SEEA Ecosystem Accounts (SEEA-EA), which characterize ecosystem extent, condition, and service supply across thirteen Holdridge life zones. The second contribution is about governance and process, documenting how the accounting program was built and institutionalized, and how it was connected to policy decisions over time, and offering lessons on the conditions under which natural capital accounts inform governance in a developing country context. These two dimensions are inseparable; the empirical record only became available because of the institutional architecture that sustained it, and the governance lessons are only credible because the accounts generated consistent, policy-relevant evidence over two decades (Casta\u0026ntilde;eda et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vardon et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e"},{"header":"2. Conceptual Framework","content":"\u003cp\u003eEnvironmental accounting is grounded in the recognition that economies operate as subsystems of a finite biosphere, constrained by biophysical laws that conventional economic indicators were not designed to capture. Ecological economics, consolidated from the foundational contributions of Daly (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) and Costanza et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), treats economic systems as embedded within ecological systems rather than separate from them. This framing has direct implications for measurement: if economic activity depletes the ecological foundations on which it depends, a GDP-centric accounting framework will systematically misrepresent the sustainability of that growth (Daly, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Dasgupta, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The key conceptual distinction is between weak sustainability, which assumes that natural and produced capital are substitutable, and strong sustainability, which recognizes that certain ecological functions are irreplaceable and that some degradation thresholds are irreversible (Casta\u0026ntilde;eda \u0026amp; Pinillos, forthcoming).\u003c/p\u003e \u003cp\u003eNatural capital, defined as the stock of ecosystems and natural resources that generate flows of goods and services underpinning economic production and human well-being, constitutes the analytical bridge between ecological and economic frameworks (Costanza et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Millennium Ecosystem Assessment, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Ecosystem services span provisioning services such as food and freshwater, regulating services including climate stabilization and watershed regulation, and cultural services encompassing recreation and spiritual values (D\u0026iacute;az et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Because most of these services flow outside markets, they remain invisible in conventional national accounts, generating incentives that systematically undervalue and degrade ecosystems. The Adjusted Net Savings indicator partially closes this gap by correcting standard savings measures for natural capital depletion and pollution damage, offering a first approximation of whether an economy is building or consuming its asset base (World Bank, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEnvironmental-economic accounting operationalizes these concepts within a statistically rigorous framework. The SEEA provides the methodological architecture for linking ecological data to national accounts, enabling systematic and consistent measurement of how natural assets are extracted, used, and degraded in relation to economic activity. This approach is fundamentally different from isolated environmental indicators because it integrates ecological and economic information within the same statistical structure, making trade-offs and dependencies visible to policymakers (Vardon et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The transition from measuring individual natural assets under the SEEA Central Framework to measuring ecosystems as integrated spatial assets under the SEEA Ecosystem Accounts represents an advance in ecological precision: asset-by-asset accounts reveal sectoral pressures, while ecosystem accounts reveal the functional interdependencies across landscapes that determine the long-term supply of services (Edens et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The methodological challenges of this transition, including spatial unit definition, service prioritization, and physical-to-monetary linkage under data-scarce conditions, are precisely those that Guatemala\u0026rsquo;s program confronted and resolved over two decades of practice (Casta\u0026ntilde;eda \u0026amp; Pinillos, forthcoming; Casta\u0026ntilde;eda et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe institutional dimension of accounting is as important as the technical one. Accounts are not self-executing; they require institutional embedding to translate measurement into governance. Research on the conditions under which natural capital accounts influence policy consistently identifies demand-side alignment, political ownership, and capacity continuity as critical success factors (Ruijs et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vardon et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In developing country contexts, where statistical capacity is constrained and political cycles are short, sustaining an accounting program long enough to generate policy-relevant evidence requires deliberate institutional architecture. Guatemala\u0026rsquo;s experience offers a case study in how this architecture can be built and sustained, providing a basis for examining both the empirical content of two decades of accounts and the governance lessons that record contains (Casta\u0026ntilde;eda \u0026amp; Pinillos, forthcoming).\u003c/p\u003e"},{"header":"3. Methods and Data","content":"\u003cp\u003eGuatemala's environmental-economic accounting program has unfolded across three broadly distinguishable phases spanning more than two decades. The first phase (approximately 2006\u0026ndash;2013) was initiated through a public-academic partnership between INE, Banguat, and IARNA-URL, with financial support from the Dutch government. This partnership applied the SEEA Central Framework methodology to compile accounts for forests, water, energy and emissions, fisheries, subsoil assets, land, and ecosystems. The resulting work covered the period 2001\u0026ndash;2006 initially and later extended to 2010. Results for 2001\u0026ndash;2006 were published in several sets of publications and dissemination documents (see Annex for a full list of publications), and the culmination of this work was published as the Sistema de Contabilidad Ambiental y Economica de Guatemala 2001\u0026ndash;2010, a comprehensive statistical compendium constituting one of the most complete SEEA Central Framework implementations in the developing world at that time (INE, Banguat and IARNA-URL, 2013).\u003c/p\u003e \u003cp\u003eThe second phase (2014\u0026ndash;2019) was formalized through Guatemala's participation in the Wealth Accounting and the Valuation of Ecosystem Services (WAVES) Global Partnership, led by the World Bank. WAVES extended and institutionalized the accounting work, updating Central Framework accounts, developing ecosystem accounts under the SEEA-EA standard, and piloting an environmental-agriculture account, making Guatemala the first WAVES country to do so (World Bank, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Little additional work emerged during the period following the WAVES program, including the publication of the watershed-scale ecosystem service accounts (Pinillos et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the third phase, the most tangible result and indicator of sustained institutionalization emerged. The Bank of Guatemala formally incorporated environmental-economic accounting into its Institutional Strategic Plan 2022\u0026ndash;2026 (PEI 2022\u0026ndash;2026), making it one of the few central banks in Latin America to include natural capital accounting as an explicit strategic objective (Banguat, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This commitment led to the creation of a dedicated Environmental Accounting Unit within the Departamento de Estadisticas Macroecon\u0026oacute;micas, staffed by a multidisciplinary team of approximately twelve professionals from economics, ecology, geography, and statistics. As a result of this institutional consolidation, Guatemala now reports active preparation of a comprehensive set of accounts covering both SEEA Central Framework and SEEA Ecosystem Accounting modules, a trajectory whose governance implications are discussed later in the paper.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the structure of the System of Environmental-Economic Accounting (SEEA) in Guatemala, linking accounting components to thematic accounts. It shows how assets, flows, expenditures, and aggregates are distributed across different environmental areas such as forests, water, energy, and land. The table highlights that natural resources and products are widely covered across multiple accounts, reflecting their central role in environmental-economic interactions. Ecosystem-related elements appear mainly in land and ecosystem accounts, indicating a more specialized treatment. Flows such as residues and ecosystem inputs demonstrate how environmental pressures and services are recorded within the system. Expenditures on environmental protection and resource management are consistently included, showing the importance of tracking policy responses. Finally, aggregates like depletion and complementary indicators provide summary measures for analysis. Overall, the table illustrates the SEEA framework's integrated and modular nature, supporting comprehensive environmental accounting and informed decision-making.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMatrix of the SEEA Accounting Framework Structure in Guatemala\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccounting structure of SEEA (by account)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCRH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCRS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCEE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCTE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCRPA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCGTA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssets\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatural resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEcosystems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand and surface water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFlows\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatural resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEcosystem inputs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProducts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExpenditures and transactions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental protection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResource management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAggregates\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepletion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDegradation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplementary indicators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e☑\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote.\u003c/em\u003e Symbols indicate the presence or relevance of each element within the account: ☑ (main inclusion), ✓ (partial inclusion), * (applicable/recorded). Abbreviations: CB\u0026thinsp;=\u0026thinsp;Forest Account; CRH\u0026thinsp;=\u0026thinsp;Water Resources Account; CRS\u0026thinsp;=\u0026thinsp;Subsoil Resources Account; CEE\u0026thinsp;=\u0026thinsp;Energy and Emissions Account; CTE\u0026thinsp;=\u0026thinsp;Land and Ecosystems Account; CRPA\u0026thinsp;=\u0026thinsp;Environmental Protection Expenditure Account; CR\u0026thinsp;=\u0026thinsp;Residues Account; CGTA\u0026thinsp;=\u0026thinsp;Environmental Transactions Account. Adapted from INE (Guatemala).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e identifies the main national institutions responsible for providing data to support the System of Environmental-Economic Accounting (SEEA) in Guatemala. The table shows that environmental information is generated through a decentralized network of specialized institutions, each contributing according to its sectoral mandate. For example, forest data are primarily produced by forestry and conservation agencies, whereas water-related information is produced by environmental authorities, statistical offices, and hydrometeorological institutions. Energy and subsoil accounts rely heavily on the Ministry of Energy and Mines, complemented by market and environmental entities. Agricultural institutions play a key role in the collection of land, ecosystem, and fisheries data. Meanwhile, waste and emissions data are largely managed at the municipal level, reflecting local responsibilities. Financial and policy-related information is provided by fiscal and planning institutions. Overall, the table highlights the interinstitutional coordination required to compile environmental accounts and underscores the importance of integrating diverse data sources for comprehensive environmental-economic analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMain National Institutions Providing Information to the SEEA in Guatemala\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMain information sources\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eForest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNational Forest Institute (INAB); National Council of Protected Areas (CONAP); National Institute of Statistics (INE)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWater\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Environment and Natural Resources (MARN); National Institute of Statistics (INE); National Institute of Seismology, Volcanology, Meteorology and Hydrology (INSIVUMEH)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubsoil\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Energy and Mines (MEM)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnergy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Energy and Mines (MEM); Wholesale Market Administrator (AMM); National Institute of Statistics (INE); Ministry of Environment and Natural Resources (MARN)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLand and ecosystems\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Agriculture, Livestock and Food (MAGA); National Institute of Statistics (INE)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFisheries resources\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFisheries and Aquaculture Unit of MAGA (UNIPESCA); Ministry of Agriculture, Livestock and Food (MAGA)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWaste and emissions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMunicipal Development Institute (INFOM); Municipality of Guatemala\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExpenditures and transactions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Public Finance (MINFIN); Tax Administration System (SAT); Municipal Development Institute (INFOM); Secretariat of Planning and Programming of the Presidency (SEGEPLAN)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003eNote.\u003c/em\u003e Adapted from INE (Guatemala). Institutional acronyms are preserved in Spanish.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe results are organized in two parts reflecting Guatemala's two-tier accounting program. Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e presents key findings from the SEEA Central Framework accounts, summarizing what two decades of asset-by-asset accounting have revealed about natural capital depletion across forests, water, energy, and related sectors. Sections \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e to \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e4.6\u003c/span\u003e then present the SEEA-EA ecosystem accounts, covering ecosystem extent, changes over time, ecosystem condition, ecosystem service supply, and the economic relevance of those services.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.1. SEEA Central Framework accounts\u003c/h2\u003e \u003cp\u003eGuatemala's SEEA Central Framework accounts, compiled for the period 2001\u0026ndash;2010 and updated through 2019, provide the most comprehensive empirical record of natural capital dynamics available for any Central American economy (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results reveal a consistent pattern: the country's economic growth was systematically sustained by the drawdown of natural capital stocks that did not appear in conventional national accounts (IARNA-URL, 2012). The forest accounts document that Guatemala lost approximately 40% of its forest cover between 1970 and 2005, with an annual deforestation rate of 1.7%, roughly three times the regional average for Latin America and the Caribbean. Over 95% of timber extraction occurred outside formal institutional control, indicating that the legal economy captured only a fraction of actual forest use (Banguat and IARNA-URL, 2009).\u003c/p\u003e \u003cp\u003eThe real contribution of forests to the national economy was estimated at 3.15% of GDP in 2001 and 2.57% in 2006, compared with approximately 1% recorded in conventional national accounts, a systematic undervaluation of more than threefold. The cumulative economic cost of forest degradation through erosion loss and carbon stock depletion reached approximately USD 374\u0026nbsp;million between 1991 and 2003 (INE, Banguat, and IARNA-URL, 2013; Casta\u0026ntilde;eda et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The water accounts documented the cost of water-related diseases from contaminated sources at 0.97% of GDP annually. The flow accounts further revealed that the agricultural sector consumed 53% of national water while contributing only 12% of GDP, a structural dependency ratio that makes visible the hidden role of natural capital in economic performance (Banguat and IARNA-URL, 2009).\u003c/p\u003e \u003cp\u003eThe energy accounts reveal that biomass (primarily firewood) remains the dominant household energy source in rural Guatemala, with total greenhouse gas emissions from energy combustion reaching 45.6\u0026nbsp;million tonnes of CO2 equivalent over the accounting period (Casta\u0026ntilde;eda et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Natural capital represented 23.1% of total national wealth in 1995, declining to 16.3% by 2020, more than double the high-income country average of approximately 8% (INE, Banguat and IARNA-URL, 2013). At the sub-national level, the earliest SCAEI series (2001\u0026ndash;2006) estimated the combined value of forest and subsoil assets at approximately GTQ 666\u0026nbsp;billion by 2006, nearly twice the value of produced capital in the same year, underscoring the structural weight of natural assets in Guatemala's national balance sheet (Banguat and IARNA-URL, 2009). The Adjusted Net Savings (ANS) indicator, which corrects GDP-based savings for natural capital depletion and pollution damage, averaged just 1.6% of GNI during 1995\u0026ndash;2016 and turned negative in several years, a signal that Guatemala was, in aggregate, consuming its asset base rather than building it (Casta\u0026ntilde;eda et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; World Bank, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes key quantitative results across the Central Framework account domains.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eKey Quantitative Results from Guatemala's Environmental-Economic Accounts\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuantitative result\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eForest accounts\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest cover loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40% of the total forest cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1970\u0026ndash;2005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual deforestation rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7% per year (3\u0026times; the LAC regional average)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2001\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTimber extraction without institutional oversight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOver 95% of total volume extracted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2001\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReal contribution of forests to GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.15% (2001) and 2.57% (2006) vs. ~1% in conventional accounts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2001\u0026ndash;2006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic cost of forest degradation (erosion and carbon)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2,919.4\u0026nbsp;million (~\u0026thinsp;USD 374.3\u0026nbsp;million) accumulated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1991\u0026ndash;2003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWater and agriculture accounts\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain water demand sectors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaize (rain-fed) and sugarcane (irrigated); high climate variability dependence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2001\u0026ndash;2013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSugarcane share of total crop volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70% of total agricultural production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2001\u0026ndash;2013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop contribution to GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9% of GDP; other agricultural products, 2% additional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2001\u0026ndash;2013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnergy accounts\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary energy source in rural households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirewood (biomass), with negative impact on forest cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2001\u0026ndash;2013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal GHG emissions from energy combustion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.6\u0026nbsp;million tonnes CO₂ equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2001\u0026ndash;2013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy intensity of electricity sector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.2 TJ per million GTQ of GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2001\u0026ndash;2013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLand and ecosystem accounts (Central Framework)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEcoregions with critical fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 of 14 ecoregions with compromised ecological integrity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand degradation and risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10% of territory highly degraded; 63% at high degradation risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual cost of soil degradation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55% of GDP per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost of waterborne diseases from contaminated sources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97% of GDP per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFisheries accounts\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFisheries and aquaculture contribution to GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19%\u0026ndash;0.25% of GDP; over 50% of production exported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2001\u0026ndash;2005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment in fisheries and aquaculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,133 persons directly employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2001\u0026ndash;2013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth and Adjusted Net Savings\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatural capital share of total wealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21% of total wealth (more than double the global average of ~\u0026thinsp;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatural capital trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecline from 23% to 21% of total wealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1995\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted Net Savings (ANS) average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6% of GNI; several years with negative values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1995\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eNote.\u003c/b\u003e GTQ\u0026thinsp;=\u0026thinsp;Guatemalan quetzal; TJ\u0026thinsp;=\u0026thinsp;terajoules; ANS\u0026thinsp;=\u0026thinsp;Adjusted Net Savings; LAC\u0026thinsp;=\u0026thinsp;Latin America and Caribbean; GHG\u0026thinsp;=\u0026thinsp;greenhouse gas. GDP and GNI in constant prices where available. Sources: Banguat and IARNA-URL (2009); INE, Banguat and IARNA-URL (2013); Casta\u0026ntilde;eda et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); World Bank (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Ecosystem extent\u003c/h2\u003e \u003cp\u003eThe accounts adopt Holdridge life zones as the unit of analysis for ecosystem extent, treating each of the 13 bioclimatic zones identified for Guatemala as a distinct ecosystem type. This choice follows the rationale that life zones, delimited by combinations of mean annual biotemperature, precipitation, and potential evapotranspiration, capture the floristic physiognomy and structural characteristics that differentiate ecosystems across Guatemala's highly variable altitudinal and precipitation gradient (IARNA-URL, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, as cited in Banco Mundial et al., 2021). The system has an empirical and objective basis and is widely recognized as appropriate for tropical and subtropical vegetation classification, offering greater ecological resolution than administratively derived zoning systems (Lugo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Its main strengths for ecosystem mapping lie in its grounding in the climatic drivers of ecosystem processes, recognition of plants' ecophysiological responses, and hierarchical use of additional mapping criteria at finer levels of ecological complexity. The system also offers two operational advantages that are particularly relevant for a national accounting exercise: the classifications can be georeferenced, enabling spatially differentiated analysis and policy recommendations, and they can be updated dynamically as new climatic data become available (Banco Mundial et al., 2021). Because life zones integrate the climatic drivers that determine vegetation structure and composition, their spatial extent serves as a meaningful proxy for the territorial extent of each ecosystem type.\u003c/p\u003e \u003cp\u003eIn Guatemala, the humid tropical forest (bh-T) is the most extensive ecosystem, covering 3.43\u0026nbsp;million hectares and representing 31.7% of national territory. It is followed by the dry tropical forest (bs-T) at 2.08\u0026nbsp;million hectares (19.2%). Together with the premontane humid forest (bh-PMT) and the lower montane humid forest (bh-MBT), these four life zones account for nearly 77% of the country's total area. Approximately 6.47\u0026nbsp;million hectares (roughly 60%) of national territory supports natural ecosystems, while 4.29\u0026nbsp;million hectares (approximately 40%) are classified as artificial ecosystems, including croplands, pastures, and urban areas. Full extent data by life zone are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eEcosystem Extent by Holdridge Life Zone, Guatemala (2012)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture province\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLife zone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% of country area\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque muy seco tropical (bms-T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81,887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque seco tropical (bs-T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e432,226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque seco premontano tropical (bs-PMT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147,803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHumid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque h\u0026uacute;medo tropical (bh-T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,427,543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.72%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque h\u0026uacute;medo premontano tropical (bh-PMT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e704,248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.52%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque h\u0026uacute;medo montano bajo tropical (bh-MBT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e338,283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery humid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque muy h\u0026uacute;medo tropical (bmh-T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e963,265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque muy h\u0026uacute;medo premontano tropical (bmh-PMT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e648,133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque muy h\u0026uacute;medo montano bajo tropical (bmh-MBT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e273,898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.53%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque muy h\u0026uacute;medo montano sub-andino tropical (bmh-ST)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e170,516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.58%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePluvial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque pluvial premontano tropical (bp-PMT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque pluvial tropical (bp-T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86,413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque pluvial sub-andino tropical (bp-SAT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41,010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10,820,725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote.\u003c/b\u003e Life zone abbreviations: bms\u0026thinsp;=\u0026thinsp;bosque muy seco; bs\u0026thinsp;=\u0026thinsp;bosque seco; bh\u0026thinsp;=\u0026thinsp;bosque h\u0026uacute;medo; bmh\u0026thinsp;=\u0026thinsp;bosque muy h\u0026uacute;medo; bp\u0026thinsp;=\u0026thinsp;bosque pluvial; T\u0026thinsp;=\u0026thinsp;tropical; PMT\u0026thinsp;=\u0026thinsp;premontane tropical; MBT\u0026thinsp;=\u0026thinsp;lower montane tropical; MT\u0026thinsp;=\u0026thinsp;montane tropical; SAT\u0026thinsp;=\u0026thinsp;subandean tropical. Source: IARNA-URL (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); Banco Mundial et al. (2021, Cuadro 1).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eForest cover dynamics in the 1991\u0026ndash;2001 period, when Guatemala's annual deforestation rate was estimated at approximately 1.43% of forest cover per year, represented the most acute phase of forest loss. Subsequent periods showed some moderation in annual net loss rates, associated in part with the consolidation of the National System of Protected Areas (SIGAP) and the expansion of forest incentive programs. The 2010\u0026ndash;2014 period shows continued but lower annual losses, with net deforestation estimated at 17,000\u0026ndash;19,000 hectares per year nationally. The persistence of high household fuelwood dependence throughout this period was a primary driver of continued pressure on forest cover (van Kempen et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Net forest loss over the full 1991\u0026ndash;2014 period reached approximately 1.07\u0026nbsp;million hectares, distributed unevenly across life zones as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eNet Forest Loss by Life Zone and Sub-period, Guatemala (1991\u0026ndash;2014)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLife zone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1991\u0026ndash;2001\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2001\u0026ndash;2006\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2006\u0026ndash;2010\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2010\u0026ndash;2014\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNet (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnn. (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNet (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnn. (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNet (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAnn. (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNet (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAnn. (ha)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebms-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebs-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-37,732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4,249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-50,025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8,422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-44,571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-11,668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-20,992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-5,248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebs-PMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-15,447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1,740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-10,936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1,841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-11,372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2,977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebh-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-307,731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-34,654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-236,069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-39,742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-80,720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-21,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-92,747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-23,187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebh-PMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-93,183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10,494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-21,341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3,593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-30,717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-8,041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-32,189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-8,047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebh-MBT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-25,821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2,908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17,452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18,977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4,968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35,687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8,922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebmh-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-27,985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3,152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7,871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1,325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13,063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3,266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebmh-PMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-39,477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4,446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-8,626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2,258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebmh-MBT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5,305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebmh-MT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6,939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11,718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebp-PMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1,415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebp-MT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebp-SAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-562,089\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-63,298\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-285,554\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-48,073\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-146,212\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-38,275\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-75,350\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-18,838\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eNote.\u003c/b\u003e Negative values indicate net forest loss (ha); positive values indicate net gain. Ann. = annual net change (period total\u0026thinsp;\u0026divide;\u0026thinsp;years). Sources: Banco Mundial et al. (2021, Cuadros 5.1\u0026ndash;5.4); UVG, INAB, CONAP \u0026amp; URL (2006, 2011, 2012); GIMBUT (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Life zone abbreviations: bms\u0026thinsp;=\u0026thinsp;bosque muy seco; bs\u0026thinsp;=\u0026thinsp;bosque seco; bh\u0026thinsp;=\u0026thinsp;bosque h\u0026uacute;medo; bmh\u0026thinsp;=\u0026thinsp;bosque muy h\u0026uacute;medo; bp\u0026thinsp;=\u0026thinsp;bosque pluvial; T\u0026thinsp;=\u0026thinsp;tropical; PMT\u0026thinsp;=\u0026thinsp;premontane tropical; MBT\u0026thinsp;=\u0026thinsp;lower montane tropical; MT\u0026thinsp;=\u0026thinsp;montane tropical; SAT\u0026thinsp;=\u0026thinsp;subandean tropical. Source: IARNA-URL (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); Banco Mundial et al. (2021, Cuadro 1).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Ecosystem extent changes over time\u003c/h2\u003e \u003cp\u003eForest cover dynamics within each Holdridge life zone track how ecosystem extent has changed over time, providing the temporal dimension of the extent accounts. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents net forest loss by life zone and sub-period from 1991 to 2014. The data draw on five national forest cover maps produced by the Interinstitutional Forest and Land Use Monitoring Group (GIMBUT) and organized by the same 13 life zone units used in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The sub-period structure allows identification of trends in deforestation pressure and recovery that a single-point comparison would obscure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Ecosystem condition\u003c/h2\u003e \u003cp\u003eForest cover dynamics within each life zone are used as the primary proxy for ecosystem condition. This approach is grounded in the recognition that forest cover stability is strongly indicative of an ecosystem's broader functional integrity. Research consistently shows that deforestation disrupts the soil, water, and biogeochemical cycles that underpin ecosystem service provision: deforestation of tropical forest profoundly impacts soil properties and functions, including nutrient storage and recycling, carbon storage and greenhouse gas emissions, erosion resistance, and water storage, drainage, and filtration, with changes continuing for decades after forest clearing and eventually extending to deep subsoils (van Straaten et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). At the landscape scale, forest patches embedded within deforested landscapes experience significant alterations in nutrient cycling and carbon stocks, reductions in frugivory and seed dispersal, and a simplification of ecological interactions, with forest specialist species exhibiting reduced diversity (Leal et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin ecosystem accounting frameworks, tree cover density has been validated as a core structural indicator of forest condition under the SEEA Ecosystem Condition Typology (Cz\u0026uacute;cz et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Maes et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and forest degradation has multiple negative consequences, reducing economic output, slashing the capacity of forests to deliver ecosystem services such as flood protection and nature-based recreation, and contributing to biodiversity loss. From an accounting perspective, the availability of multi-date national forest cover maps produced by the Interinstitutional Forest and Land Use Monitoring Group (Gimbut) for 1991, 2001, 2006, 2010, and 2014 makes forest cover the most temporally consistent and spatially resolved condition indicator accessible at the ecosystem level in Guatemala. While the Guatemalan ecosystem accounts acknowledges that ideal condition assessment would additionally incorporate indicators such as species richness, soil erosion rates, and water availability, data constraints rendered most of these inviable at the national scale for the reference period; forest cover dynamics therefore serve as the best available approximation of ecosystem condition given the current state of national information systems (Banco Mundial et al., 2021).\u003c/p\u003e \u003cp\u003eThe Guatemala Ecosystem Account (Cuenta de Ecosistemas de Guatemala [EA]; Banco Mundial et al., 2021) assesses ecosystem condition through several indicators derived from forest cover and protected-area dynamics. The present section reports a subset of two, those for which sufficient spatial and temporal data were available and that are most directly relevant to the analysis developed in this paper: (1) the Normalized Difference Vegetation Index (NDVI), (2) isolation and external pressure on legally protected areas.\u003c/p\u003e \u003cp\u003e \u003cb\u003eNDVI as a Proxy for Ecological Integrity\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Normalized Difference Vegetation Index (NDVI) provides a proxy for the ecological integrity of forested areas, capturing vegetation health and density through the relationship between near-infrared reflectance\u0026mdash;which is high in healthy plant tissue\u0026mdash;and visible red reflectance, absorbed by chlorophyll during photosynthesis (El-Gammal et al., 2014, as cited in Banco Mundial et al., 2021). Index values range from \u0026minus;\u0026thinsp;1 to 1: values below 0.2 correspond to open water or bare ground; 0.2\u0026ndash;0.4 to sparse vegetation; 0.4\u0026ndash;0.6 to moderately dense vegetation; and values above 0.6 to dense, structurally complex vegetation (Al-doski et al., 2013, as cited in Banco Mundial et al., 2021). For the EA, NDVI was calculated from Landsat 8 imagery (30 m resolution) acquired between 2017 and 2019 (United States Geological Survey [USGS], 2020). The upper range (\u0026gt;\u0026thinsp;0.6) was further subdivided to improve discrimination among forest conditions, yielding the six-class scale presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe densest and healthiest forests (NDVI\u0026thinsp;\u0026gt;\u0026thinsp;0.8) were found in the Sierra de las Minas mountain range, the Sierra de los Cuchumatanes, and in humid tropical forest areas within the Verapaces region. These were followed by dry tropical forest (bs-T), which also exhibited relatively high NDVI values despite lower annual precipitation. In contrast, forests north of the volcanic cordillera showed the lowest vegetation density values. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e summarises the dominant NDVI ranges by ecosystem.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eDominant NDVI Range and Vegetation Condition by Ecosystem Type (2017\u0026ndash;2019)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEcosystem (Holdridge zone)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHumidity province\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDominant NDVI range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVegetation condition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebms-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque muy seco tropical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u0026ndash;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebs-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque seco tropical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u0026ndash;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDense (among highest)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebs-PMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque seco premontano tropical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u0026ndash;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebh-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque h\u0026uacute;medo tropical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHumid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u0026ndash;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDense (esp. Verapaces)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebh-PMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque h\u0026uacute;medo premontano tropical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHumid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u0026ndash;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebh-MBT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque h\u0026uacute;medo montano bajo tropical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHumid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u0026ndash;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDense (Sierra de las Minas)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebmh-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque muy h\u0026uacute;medo tropical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery humid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u0026ndash;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDense\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebmh-PMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque muy h\u0026uacute;medo premontano tropical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery humid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u0026ndash;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebmh-MBT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque muy h\u0026uacute;medo montano bajo tropical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery humid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u0026ndash;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDense\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebmh-MT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque muy h\u0026uacute;medo montano tropical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery humid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u0026ndash;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDense (Sierra de los Cuchumatanes)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebp-PMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque pluvial premontano tropical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePluvial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u0026ndash;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDense\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebp-MT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque pluvial montano tropical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePluvial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u0026ndash;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDense\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebp-SAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBosque pluvial subandino tropical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePluvial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u0026ndash;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSparse (rocky/open)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e Based on Banco Mundial et al. (2021), using USGS Landsat 8 imagery (2017\u0026ndash;2019). NDVI values represent approximate dominant ranges derived from the spatial distribution analysis reported in the EA. Values for individual protected areas or localities may differ. Forest vegetation density was classified using the NDVI following six categories: values above 0.85 indicate very dense, structurally complex intact forest; values between 0.80 and 0.85 correspond to healthy, very dense forest with high biomass and minimal stress; values between 0.60 and 0.80 reflect dense forest with continuous canopy cover; values between 0.40 and 0.60 indicate moderate, fragmented or disturbed forest; values between 0.20 and 0.40 represent sparse vegetation such as shrubland or degraded areas; and values below 0.20 correspond to areas with no vegetation, including bare soil, water bodies, or urban surfaces (adapted from Banco Mundial et al., 2021, Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, based on Al-doski et al., 2013).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eExternal Pressure and Isolation of Legally Protected Areas (2001\u0026ndash;2014)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn addition to internal deforestation, the EA assessed the extent to which protected areas are under pressure from surrounding land-use change and are becoming progressively isolated from other natural areas. Following established conservation planning practice, pressure was operationalized using deforestation recorded in a 1 km buffer around each protected area (proxying immediate edge pressure), while isolation was assessed through forest loss in a 10 km buffer (proxying landscape-scale connectivity; Banco Mundial et al., 2021).\u003c/p\u003e \u003cp\u003eThe analysis focused on 37 protected areas belonging to the three most restrictive management categories (I, II, and VI). Systematic deforestation in both buffer widths was recorded for all areas assessed, indicating a trend of progressive isolation that compromises the viability of large-mammal populations and reduces the provision of ecosystem services, including hydrological regulation, carbon sequestration, and cultural services. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e summarises the qualitative isolation and pressure profile for a selection of these protected areas.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eIsolation and External Pressure on Selected Legally Protected Areas (2001\u0026ndash;2014)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtected area (ecosystem)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSize class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePressure (1 km buffer)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIsolation risk (10 km buffer)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverall level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaguna del Tigre NP (bs-T / bmh-T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSierra del Lacand\u0026oacute;n NP (bs-T / bmh-T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMirador-R\u0026iacute;o Azul NP (bs-T / bmh-T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaya Biosphere Reserve (overall)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery large\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSierra de las Minas BR (bmh-MBT / bh-T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u0026ndash;moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtitl\u0026aacute;n Volcano NP (bmh-PMT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCritical\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrifinio (bmh-MBT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e Pressure reflects deforestation intensity in the 1 km buffer zone; isolation risk reflects deforestation intensity in the 10 km buffer zone. Qualitative levels (low/moderate/high/severe) are derived from the relative deforestation rates reported in Banco Mundial et al. (2021). NP\u0026thinsp;=\u0026thinsp;National Park; BR\u0026thinsp;=\u0026thinsp;Biosphere Reserve.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Maya Biosphere Reserve and its connecting biological corridors face particularly severe pressure: losses in the four connecting corridors accounted for 84% of the total forest area that disappeared at the national level between 2001 and 2014, illustrating how progressive isolation amplifies the ecological risks already identified at the individual protected area level (Banco Mundial et al., 2021).\u003c/p\u003e \u003cp\u003eTaken together, the indicators reported in this section reveal a consistent pattern of ecosystem degradation across Guatemala: vegetation quality remains highest in mountain ranges and remote humid areas; formal protection does not guarantee the preservation of forest cover; protected areas are becoming increasingly isolated due to surrounding deforestation; and forest loss outside protected areas is most acute in the ecosystems that already experienced the greatest historical pressures. These findings underscore the need for complementary conservation instruments\u0026mdash;including landscape-level planning, biological corridor management, and land-use policies for unprotected forestlands\u0026mdash;to sustain the ecosystem services documented in the EA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Ecosystem services supply\u003c/h2\u003e \u003cp\u003eThe SEEA-EA ecosystem accounts draw on a protected area inventory covering 189 of Guatemala\u0026rsquo;s 334 SIGAP areas and representing 95% of total protected area extent. Regulating services were the most frequently reported category overall, with regulation of hydrological flow and the water cycle accounting for 122 reports across all management categories. This reflects the critical role of protected forests in maintaining watershed function across the country's 38 major river basins (Pinillos et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Soil erosion control (59 reports) and regulation of extreme events (43 reports) were the next most-cited regulatory services. Cultural services were the most reported category by number of records when Type V (protected landscapes) areas are included. Biodiversity conservation was the single most frequently documented service across the full dataset (183 records), followed by recreation and ecotourism (85 records), cultural and spiritual significance for Indigenous communities (64 records), and research and education services (62 records). Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents ecosystem service reports by SIGAP management category across 189 of 334 assessed protected areas, representing 95% of protected area extent.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eEcosystem Services Reported in Protected Areas by SIGAP Management Category\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003eProtection type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEcosystem service\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvisioning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCultivated plants \u0026amp; animals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWild plants for food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRearing animals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWild animals for food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAquatic transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTimber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-timber forest products\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegulating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulation of hydrological flows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater purification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil erosion control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePollination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClimate regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePest \u0026amp; disease regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiodiversity conservation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecreation \u0026amp; tourism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpiritual \u0026amp; cultural values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResearch \u0026amp; education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiophysical characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAesthetic values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e152\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e151\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e72\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e136\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e620\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eNote.\u003c/b\u003e Values\u0026thinsp;=\u0026thinsp;number of service reports. 189 of 334 SIGAP areas assessed, representing 95% of protected area extent and 29% of national territory. Type I\u0026thinsp;=\u0026thinsp;strict reserves; Type II\u0026thinsp;=\u0026thinsp;national parks; Type III\u0026thinsp;=\u0026thinsp;natural monuments; Type IV\u0026thinsp;=\u0026thinsp;habitat/species management; Type V\u0026thinsp;=\u0026thinsp;protected landscapes; Type VI\u0026thinsp;=\u0026thinsp;managed resource areas. Source: Banco Mundial et al. (2021, Cuadro 11.1).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Economic relevance of ecosystem services\u003c/h2\u003e \u003cp\u003eThe monetary valuation synthesis covers approximately 1.73\u0026nbsp;million hectares across Guatemala's natural areas. The total estimated annual flow amounts to approximately USD 843\u0026nbsp;million, equivalent to roughly 3.6% of Guatemala's 2019 GDP. This encompasses 30 specific services across the three SEEA-EA categories and reflects a partial and conservative estimate, as it covers only areas where valuation studies were available. Cultural services represent the largest share of estimated economic value at approximately USD 439\u0026nbsp;million per year (52% of total), dominated by the composite non-use value of intact natural areas (USD 378\u0026nbsp;million/year) and tourism and recreation (USD 58\u0026nbsp;million/year). Regulating services account for approximately USD 294\u0026nbsp;million per year (35%), led by nutrient cycling (USD 263\u0026nbsp;million/year) and hydrological regulation (USD 15.5\u0026nbsp;million/year). Provisioning services account for approximately USD 111\u0026nbsp;million per year (13%), led by aquatic transport (USD 39.7\u0026nbsp;million/year) and fisheries (USD 32.6\u0026nbsp;million/year). The full breakdown is presented in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eEstimated Annual Economic Value of Ecosystem Services in Guatemala\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEcosystem service\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual value (USD/year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiophysical characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e377,758,388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47,039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTourism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e600,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecreation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,400,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegulating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbon sequestration (REDD+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,200,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydrological regulation (hydropower)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,800,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater supply regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,100,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil erosion control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e950,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvisioning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAquatic transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39,700,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e320,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTimber and non-timber forest products\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e480,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreshwater supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33,300,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e290,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAll services (partial estimate)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e843,158,388\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e12,837,039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote.\u003c/b\u003e Values in nominal USD. Area\u0026thinsp;=\u0026thinsp;geographic extent of each valuation case study. Cultural services account for 52% of total value; regulating 35%; provisioning 13%. The total (USD 843\u0026nbsp;million/year) represents approximately 3.6% of Guatemala's 2019 GDP and is a partial and conservative estimate. Sources: Banco Mundial et al. (2021, Cuadro 12.1); IARNA-URL (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion: What Two Decades of Accounts Reveal","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5.1 What Does the Data Show\u003c/h2\u003e \u003cp\u003eGuatemala's two-decade accounting record yields a convergent empirical finding: the country's economy is structurally more dependent on natural capital than conventional statistics suggest, and that capital is being depleted faster than it is being replenished. Forest contributions to GDP were estimated at more than three times the value recorded in conventional national accounts; water services sustain agriculture and energy production in ways invisible to GDP; and ecosystem services generate an estimated USD 843\u0026nbsp;million per year in economic value that never enters the ledger (Casta\u0026ntilde;eda et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Banco Mundial et al., 2021). These findings directly substantiate the ecological economics argument that economic systems are embedded within, and ultimately constrained by, the ecological systems that sustain them (Costanza et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Daly, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). But the program also reveals something equally important: making this dependency visible requires sustained institutional investment. The patterns documented here only became legible after more than twenty years of continuous accounting effort, and their policy relevance depended on how the program was designed and embedded in governance structures from the outset.\u003c/p\u003e \u003cp\u003eThe conditions account for land-use change as the primary driver of ecosystem transformation. Agricultural expansion, infrastructure development, and population pressure have converted natural ecosystems at rates consistent with broader tropical deforestation trends. The resulting fragmentation reduces ecological connectivity and degrades the processes that sustain biodiversity and ecosystem services. Condition indicators show that ecosystems in areas of intensive land conversion exhibit substantially lower ecological integrity than those in remote or formally protected regions, underscoring the value of integrated spatial planning. The valuation synthesis reinforces a point central to ecological economics: the most economically significant ecosystem services are precisely those that markets fail to price. Watershed forests regulate flows sustaining hydropower, irrigation, and urban supply. Vegetation retains soils and maintains agricultural productivity. Intact habitats generate existence values that exceed those of any extractive use. All of these benefits remain invisible in GDP (Brander et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Freeman et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Embedding these services in a national accounting framework does not resolve the political economy of land use, but it makes the cost of ignoring them legible to policymakers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Contributions to the global development of SEEA\u003c/h2\u003e \u003cp\u003eGuatemala's experience makes a distinctive contribution to the international literature on environmental-economic accounting. The WAVES Global Partnership, which Guatemala joined as a core implementing country in March 2014, documented across its eight partner countries that sustained country-level technical assistance combined with communities of practice is a necessary condition for successful institutionalization. The process must also be driven by planning or finance agencies rather than treated as a purely statistical exercise (WAVES, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ruijs et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The GPS-WAVES Annual Report 2019, which synthesized the conclusions of the WAVES program as it transitioned to the Global Program on Sustainability (GPS), confirmed this lesson at scale: across all core implementing countries, programs that achieved durable policy uptake shared three features in common. They all had government ownership at the highest level, alignment with a national development or planning framework, and continuity of technical support across political transitions (WAVES and World Bank, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Guatemala's program exemplifies all three. Accounts were produced within INE and Banguat as official national statistics. Accounting priorities were aligned with the K'atun 2032 national development plan and the National Climate Change Action Plan. The IARNA-URL partnership provided the continuity that government institutions alone could not sustain across election cycles (Casta\u0026ntilde;eda et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; World Bank, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe three successive NCA Policy Forums organized by WAVES (2016, 2017, 2018) together built a cumulative body of cross-country evidence on what makes accounts useful for governance. The 1st Policy Forum established, drawing on the experience of twelve countries, that NCA is most useful when it covers the full policy cycle of problem identification, policy design, implementation, and monitoring. The Forum also concluded that active engagement between account producers and policy users is a prerequisite for uptake, and that major policy trends including the SDGs and green growth strategies urgently need better information on natural capital (Vardon et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The 2nd Policy Forum focused specifically on how NCA can support the Sustainable Development Goals, finding that countries with longer NCA experience used accounts most effectively because they had developed multi-disciplinary technical working groups and multi-agency NCA-policy steering committees. This is precisely the governance architecture Guatemala built through its public-academic partnership. Guatemala's forest and water accounts were cited at the 2nd Forum as a case study in which NCA had been used to inform policy on disaster risk, watershed governance, and food security simultaneously (Ruijs and Vardon, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The 3rd Policy Forum, focused on climate and biodiversity, reinforced that accounts must be positioned as analytical bridges linking environmental data to the economic decisions of finance and planning ministries, a framing that Guatemala's program had adopted from its earliest phases (Vardon, Bass and Ahlroth, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe GPS Annual Report 2020\u0026ndash;2021, the first comprehensive report of the program that replaced WAVES, documented that the transition from WAVES to GPS was motivated by lessons learned across the partnership: effective institutionalization requires not only account production but also sustained demand-side uptake, capacity building, and integration with financial decision-making (World Bank, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The report confirmed that 84 percent of WAVES Plus indicators had been met or exceeded at the time of transition, but that the remaining gap was concentrated in the more complex governance integration objectives, reflecting the inherent difficulty of moving from account production to policy embedding. Guatemala's program, having operated for more than a decade before WAVES began, had a head start on this integration challenge that most partner countries lacked, and the GPS-WAVES Annual Report 2019 specifically highlighted Guatemala's trajectory as a model for countries entering the GPS program at earlier stages of institutional development (WAVES and World Bank, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe regional context amplifies Guatemala's distinctiveness. CEPAL's 2023 survey of environmental statistics across Latin America and the Caribbean, the most comprehensive regional assessment to date, found that while progress in environmental accounting has strengthened across the region over nearly 25 years of monitoring, persistent challenges in dedicated human and financial resources continue to constrain institutionalization in most countries (Alcantar Lopez et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The survey confirmed that only a small group of countries maintain sustained and institutionalized NCA programs; the majority of LAC countries, particularly in the Caribbean and Central America, have not yet implemented the SEEA framework. The principal barriers identified across the region, which include insufficient dedicated personnel, weak inter-institutional coordination, and limited linkage between accounts and decision-making processes, are precisely the conditions that Guatemala's public-academic partnership model was designed to address (Alcantar Lopez et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Casta\u0026ntilde;eda, Castillo and Matias, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Against this regional backdrop, Guatemala's twenty-year record is not merely notable in comparative terms; it is an outlier, representing a depth and continuity of institutional commitment that few countries in the region have approached.\u003c/p\u003e \u003cp\u003eThe UNSD's 2025 Global Assessment provides a further benchmark. Of the 98 countries worldwide that implement the SEEA, Guatemala is among those that compile both the SEEA Central Framework and SEEA Ecosystem Accounting at Stage III, meaning regular compilation and dissemination of accounts (UNSD, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Among Latin American and Caribbean countries, only 6 of 11 implementing countries have reached Stage III. Across all regions, the accounts Guatemala compiles, spanning energy, water, forests, land, fisheries, emissions, and ecosystem extent, condition, and services, place it among the approximately 15 countries globally with the broadest portfolio of SEEA accounts, a distinction that no other developing country in Central America has achieved. This external verification by the global SEEA monitoring system confirms that what this paper documents as a national record is also recognized as exceptional performance by the international statistical community.\u003c/p\u003e \u003cp\u003eGuatemala's distinctiveness extends to the marine dimension of natural capital accounting. The Global Ocean Accounts Partnership's 2024 assessment of ocean accounting across Latin America and the Caribbean found that approximately 93% of all published environmental-economic accounting effort in the region has focused on terrestrial ecosystems, and that Guatemala is the only country in the region with a government-driven ocean-related account under the SEEA Central Framework. This is the fisheries account, whose latest version was published in 2019 (GOAP, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The report found that while multiple countries have assessed the potential for ocean accounts, knowledge about environmental-economic accounting methodologies ranges from basic to advanced in Latin America and from limited to none in much of the Caribbean, with the number of accounts and technical expertise correlating directly with the level of sustained international support received (GOAP, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This finding underscores a broader lesson: the terrestrial ecosystem accounts documented in this paper were themselves enabled by sustained WAVES and GPS support over a decade; extending the same institutional depth to marine and coastal ecosystems, including Guatemala's Pacific and Atlantic coasts, coral reefs, mangroves, and coastal fisheries, represents a significant and tractable next frontier for the country's accounting program.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Lessons Learned: Four Conditions for Accounts to Influence Governance\u003c/h2\u003e \u003cp\u003eSynthesizing Guatemala's own trajectory with the cross-country evidence from the WAVES Policy Forums and GPS annual reports, four conditions emerge as necessary for natural capital accounts to influence governance in a developing country context. These are not hypothetical but documented patterns visible in both Guatemala's program and in comparative programs that lacked them.\u003c/p\u003e \u003cp\u003eFirst, institutional anchoring in a stable, politically insulated host is essential. The public-academic partnership anchored by IARNA-URL and embedded within INE and Banguat provided both technical credibility and continuity across multiple political cycles. The WAVES synthesis report for Guatemala noted directly that prior to WAVES, SEEA institutionalization remained fragile precisely because it lacked this dual anchoring. Accounts existed but had no durable institutional home (World Bank, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The 1st NCA Policy Forum documented the same pattern comparatively: programs hosted exclusively within government agencies were disproportionately disrupted by political transitions, while those with academic or statistical co-anchoring showed greater continuity (Vardon et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Without the university as a stable host, Guatemala's program would have been vulnerable to the frequent changes in government priorities that have derailed similar initiatives elsewhere (Casta\u0026ntilde;eda, Castillo and Matias, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe most concrete recent evidence of this institutional logic is Banguat's own trajectory. The Bank of Guatemala's Institutional Strategic Plan 2022\u0026ndash;2026 formally inscribed environmental-economic accounting as a strategic objective, resulting in the creation of a dedicated Environmental Accounting Unit within its statistics department. This step transformed accounting from a project-dependent activity into a permanent institutional function (Banguat, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This transition from project-based implementation to institutionalized production is precisely what the WAVES partnership identified as the frontier that most developing country programs had not yet crossed. Guatemala has now crossed it. The 2025 UNSD Global Assessment confirmed Guatemala's Stage III status, meaning regular compilation and dissemination, a milestone achieved by only 6 of the 11 Latin American and Caribbean countries implementing the SEEA, and by fewer than a third of African implementing countries (UNSD, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). For a developing country with Guatemala's resource constraints, this ranking reflects not statistical ambition but durable institutional architecture built over two decades.\u003c/p\u003e \u003cp\u003eSecond, demand-side alignment with live policy questions transforms accounts from statistical products into governance tools. Across the WAVES partnership, accounts produced in statistical isolation rarely influenced decisions; accounts aligned with specific policy entry points such as budget cycles, development plans, climate commitments, and disaster risk frameworks were taken up and used (Vardon et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ruijs et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The 1st Policy Forum was explicit that account production should be synchronized with recurring policy cycles when feasible, and that accounts already in place provide a ready source of information for unanticipated policy processes (Vardon et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In Guatemala, the explicit alignment between accounting priorities and the K'atun 2032 plan, the National Climate Change Action Plan, and the design of forest incentive programs created the demand-side pull that kept accounts relevant to decision-makers throughout the WAVES phase (Casta\u0026ntilde;eda et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The WAVES scoping process for Guatemala identified these policy entry points deliberately before accounts were produced, a sequencing lesson the 2nd Policy Forum highlighted as a model for other countries (Ruijs and Vardon, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThird, sequencing matters. Building foundational credibility before attempting more complex accounts reduces political risk and sustains institutional momentum. Guatemala's program established a decade of Central Framework accounts before transitioning to the more data-intensive ecosystem accounts under SEEA-EA. This sequencing built the inter-institutional trust and shared data infrastructure on which the WAVES phase depended. The 1st Policy Forum synthesis noted that programs which attempted ecosystem accounts before establishing Central Framework credibility encountered stronger institutional resistance (Vardon et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Guatemala\u0026rsquo;s trajectory, from the 2013 compendium through the WAVES ecosystem accounting phase to post-WAVES watershed-scale accounts, illustrates the staged deepening identified as one of the most replicable features of the Guatemala model for countries at earlier stages of NCA development (Pinillos et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; WAVES and World Bank, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFourth, capacity building embedded within account production rather than separated from it creates durable in-house expertise. Each phase of Guatemala's program transferred methodological knowledge to government counterparts in SEGEPLAN, MARN, INAB, and INE, creating expertise that outlasted any individual project cycle. The GPS Annual Report 2020\u0026ndash;2021 documented that the most effective WAVES-era programs combined intensive country-level technical assistance with regional communities of practice, and that this combination distinguished programs with durable capacity from those requiring repeated external support (World Bank, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This methodological depth is evidenced by the successive phases of work documented in Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (WAVES and World Bank, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The institutional externality generated by this embedded approach, including working relationships, shared data infrastructure, and inter-institutional trust, is rarely captured in project evaluations but may represent one of the most durable returns on the investment (Casta\u0026ntilde;eda, Castillo and Matias, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ruijs et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.4. Methodological Limitations and Future Directions\u003c/h2\u003e \u003cp\u003eSeveral methodological limitations merit acknowledgment. Environmental data quality and temporal coverage remain constrained in many developing countries, and condition indicators derived from remote sensing carry uncertainties related to spatial resolution and classification consistency. Monetary valuation estimates depend on methodological assumptions that vary substantially across studies, limiting cross-site comparability and precluding interpretation as precise measures of total economic value. The figures reported here are best understood as conservative lower bounds. The WAVES synthesis report for Guatemala similarly cautioned that valuation coverage was partial and that the USD 843\u0026nbsp;million estimate reflects only services where case studies were available (World Bank, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Banco Mundial et al., 2021). Future work should prioritize improving condition indicator systems, harmonizing valuation protocols across the SEEA-EA framework, and developing approaches for integrating ecosystem accounts more directly into investment appraisal and planning decisions, an agenda that the GPS program has identified as central to its next strategic phase (World Bank, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA further limitation is the incomplete coverage of marine and coastal natural capital. As documented by GOAP (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Guatemala's fisheries account is the only government-driven ocean-related SEEA account in Latin America and the Caribbean; yet the coastal and marine ecosystems that sustain fisheries, coastal protection, and blue economy activities remain outside the scope of the ecosystem accounts synthesized in this paper. Extending the accounting framework to these assets, building on the existing SEEA-CF fisheries account and the SEEA-EA methodologies developed for terrestrial ecosystems, is a priority identified in both the post-WAVES watershed accounting work (Pinillos et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and regional assessments of the ocean accounting frontier (GOAP, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Policy Implications","content":"\u003cp\u003eGuatemala's accounting program demonstrates that natural capital accounts generate governance value through three interconnected channels. They make the economic significance of ecosystems visible to decision-makers who would otherwise rely on GDP-centric statistics. They provide spatially explicit information linking ecosystem condition to specific sectoral risks in agriculture, water, energy, and climate. And they create a shared evidence base that reduces institutional fragmentation in cross-sectoral environmental governance (Casta\u0026ntilde;eda, Castillo, and Matias, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ruijs et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vardon et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Each channel has a direct counterpart in the cross-country evidence from the NCA Policy Forums documented in Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e5.2\u003c/span\u003e: the 1st Forum on institutional embedding and the full policy cycle (Vardon et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); the 2nd Forum on SDG monitoring and cross-sectoral governance (Ruijs \u0026amp; Vardon, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); and the 3rd Forum on dual-visibility accounts engaging finance and planning ministries (Vardon, Bass and Ahlroth, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn land use, the accounts revealed that approximately 40% of national territory has been converted to artificial ecosystems, with agricultural expansion the dominant driver of forest loss across nearly all life zones. Guatemala's forest incentive programs, PINPEP and PROBOSQUE, were designed and evaluated in a context where accounting data provided independent evidence of deforestation trends and their economic costs. The accounts strengthened the evidence base for their continuation and targeting (Casta\u0026ntilde;eda et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The 1st NCA Policy Forum specifically cited Guatemala's forest accounts as a case where natural capital accounting led to new regulation and the strengthening of forest institutions -- a policy uptake pathway that required the sustained institutional investment documented in this paper (Vardon et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The GPS-WAVES Annual Report 2019 likewise highlighted Guatemala's forest and land accounts as among the most policy-relevant outputs of the WAVES program in Latin America (WAVES and World Bank, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn water and climate, the hydrological condition accounts identified the life zones and watersheds most critical for maintaining the water supply underpinning hydropower, irrigation, and urban consumption. The energy and forest accounts provided independent quantification of greenhouse gas emissions from biomass combustion and carbon stock depletion, contributing evidence to Guatemala's nationally determined contributions and climate finance instruments. This dual visibility -- of environmental risk and natural capital asset value -- is precisely what climate governance requires but conventional statistics cannot provide, positioning countries like Guatemala to translate sustained accounting capacity into climate finance and adaptation investments (Dasgupta, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Edens et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor countries considering investment in natural capital accounting, Guatemala's experience, supported by the broader evidence from the WAVES partnership and GPS program, suggests that governance returns are real but require sustained commitment. They do not materialize from a single accounting exercise (Ruijs et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; World Bank, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The GPS Annual Report 2020\u0026ndash;2021 confirmed that the transition from WAVES to GPS was itself a response to this lesson, emphasising long-term institutional support over project-cycle interventions (World Bank, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The GPS-WAVES Annual Report 2019 documented that by the close of the WAVES program, core implementing countries including Guatemala had begun to serve as mentors and knowledge-sharing hubs for other countries in their regions -- a multiplier effect that only becomes available after the kind of sustained investment Guatemala's program represents (WAVES and World Bank, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The evidence from Guatemala demonstrates that governance returns from natural capital accounting accumulate over time, and that the most durable among them may be the institutional capacity, inter-agency relationships, and shared evidence culture that the process of producing accounts generates alongside the accounts themselves.\u003c/p\u003e \u003cp\u003eGuatemala\u0026rsquo;s experience also carries direct implications for the broader Latin American region. As documented in Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e5.2\u003c/span\u003e, CEPAL\u0026rsquo;s 2023 regional survey confirms that the barriers Guatemala\u0026rsquo;s public-academic model addressed remain the dominant constraints across most LAC countries (Alcantar Lopez et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The institutional architecture Guatemala developed is precisely what the GPS program has identified as needed regionally. And as discussed in Section \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e5.4\u003c/span\u003e, the blue economy dimensions of natural capital accounting remain the most significant unfinished agenda for the region, offering a tractable extension for countries with the institutional infrastructure already in place (GOAP, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eAs environmental pressures mount and the inadequacy of GDP-centric measures of progress becomes harder to ignore, the case for sustained investment in environmental-economic accounting grows stronger. This paper has synthesized Guatemala\u0026rsquo;s full accounting record, covering more than two decades of SEEA Central Framework and SEEA-EA ecosystem accounts, and has drawn out both the empirical findings and the governance lessons that record contains. What the record reveals is that the empirical and the institutional are inseparable: the structural dependency on natural capital documented in Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e4\u003c/span\u003e only became visible because the institutional architecture described in Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e5\u003c/span\u003e sustained the work long enough to generate it, and the governance lessons are only credible because the accounts they rest on are consistent and continuous. Guatemala\u0026rsquo;s experience demonstrates that environmental-economic accounting is not a one-time exercise but a long-term institutional commitment, and that the returns to that commitment, in terms of both analytical insight and governance relevance, accumulate over time.\u003c/p\u003e \u003cp\u003eThe accounts make legible what GDP cannot: that the sectors driving Guatemala's economic output -- agriculture, hydropower, forestry, and tourism -- depend on ecosystems experiencing measurable and ongoing degradation. Sustainable development in this context is not an abstract goal but a concrete accounting challenge: the stock of natural capital must be maintained, and its depletion must register in the statistics that guide investment and policy.\u003c/p\u003e \u003cp\u003eMore broadly, the study contributes to the growing literature on ecosystem accounting in two ways. It provides empirical evidence on how the SEEA-EA framework can be applied in a tropical developing economy characterized by high biodiversity and significant land-use pressures, demonstrating that accounts can be built from available data and can support policy discussions on natural capital management and sustainable development. And it contributes process evidence on what it takes to make accounts count: the institutional architecture, partnership models, sequencing decisions, and alignment with governance priorities that determined whether two decades of accounting effort produced durable policy change. Both contributions matter for the rapidly expanding community of countries now implementing the SEEA-EA framework, and both are only visible through the kind of long-horizon synthesis this paper attempts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe author is the sole author of this academic synthesis, having played a key coordinating and advisory role across the three institutional phases spanning more than two decades mentioned in the paper. Collaborative contributions to the underlying accounting work are acknowledged in the Acknowledgments section and carefully cited throughout the text. The synthesis, interpretation, and argumentation in this paper are solely the author's own. Editorial assistance and translation from Spanish to English were provided in part using AI; the author bears full responsibility for all intellectual content and conclusions.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis paper reports on more than two decades of collaborative accounting work to which many people and institutions made essential contributions. The author gratefully acknowledges all experts from the different participating agencies, including experts from INE, Banguat, INAB, MARN, and SEGEPLAN. Special recognition is due to IARNA-URL, which introduced and led the initial phases of this work in Guatemala under the guidance of the author of this paper. Financial support from the Dutch Cooperation, the World Bank, and own institutional funding from Banguat is greatly acknowledged.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlcantar Lopez, G., Malmierca Castano, A., \u0026amp; Perez Quesada, A. 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World Bank. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://documents1.worldbank.org/curated/en/824441643695834856/pdf/Global-Program-on-Sustainability-\u003c/span\u003e\u003cspan address=\"https://documents1.worldbank.org/curated/en/824441643695834856/pdf/Global-Program-on-Sustainability-\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eAnnual-Report-2020-2021.pdf\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Bank. (2024). \u003cem\u003eThe changing wealth of nations 2024: Revisiting the measurement of comprehensive wealth\u003c/em\u003e. World Bank. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldbank.org/en/publication/the-changing-wealth-of-nations\u003c/span\u003e\u003cspan address=\"https://www.worldbank.org/en/publication/the-changing-wealth-of-nations\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Natural capital, SEEA, Ecosystem accounting, Ecosystem services, Environmental-economic accounting, Guatemala, Natural capital accounting","lastPublishedDoi":"10.21203/rs.3.rs-9438795/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9438795/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGuatemala is one of the few developing countries to have sustained more than two decades of continuous of the system of environmental-economic accounting (SEEA). This paper synthesizes the accumulated record and draws out its lessons learned. Empirically, the accounts revealed a structural dependency on natural capital invisible to conventional statistics. For example, forest contributions to the economy are underestimated by more than threefold; most indicators show that Guatemala has repeatedly consumed its natural asset base rather than building it; and ecosystem services generate an estimated USD 843\u0026nbsp;million in economic value each year that never enters national accounts. The paper documents how a sustained public-academic partnership translated accounts into official statistics and embedded them in national development planning, climate policy, and forest governance. Four conditions emerge as necessary for natural capital accounts to influence governance in a developing country context: stable institutional anchoring, demand-side alignment with live policy questions, staged sequencing of account complexity, and capacity building embedded within production. This trajectory culminated in the Central Bank of Guatemala creating a permanent environmental accounting unit within the macroeconomic statistics department. Also, it earned Guatemala recognition in the 2025 United Nations Statistics Department Global Assessment as a Stage III SEEA implementer, among the top tier of developing countries.\u003c/p\u003e","manuscriptTitle":"What Two Decades of Environmental-Economic Accounting Reveal About Guatemala’s Natural Capital and Governance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 05:50:17","doi":"10.21203/rs.3.rs-9438795/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"55a73c20-a305-4e62-a831-5dc5e54cb7db","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-10T22:55:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-10T22:54:56+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T05:50:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 05:50:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9438795","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9438795","identity":"rs-9438795","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}