Projected Global and National Energy and Climate Futures using an alternative Integrated Assessment Framework

Projected Global and National Energy and Climate Futures using an alternative Integrated Assessment Framework | 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 Article Projected Global and National Energy and Climate Futures using an alternative Integrated Assessment Framework Tejal Kanitkar, T. Jayaraman, V. P. Lavanyaa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7132898/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This study details a global mitigation pathways model, ‘Model for Energy Equity and Climate Compatibility_Version.1’ (MEECC_V.1)’, with a user interface, using a modelling approach that is a significant departure from all current economy-energy-emissions models, or Integrated Assessment Models (IAMs). It uses economy-wide metrics for projecting socio-economic futures, particularly considering those that are necessary for equity considerations, including convergence of energy use and national incomes across the global North and South and equitable access to the global carbon budget. The modelling approach enables users to explore a range of energy and climate futures instead of determining them based on least cost-optimisation frameworks. The analysis uses a development-based classification of countries as opposed to the commonly used geographical classification for developing countries in current IAMs. Results for a few illustrative scenarios are presented that demonstrate how the model enables the assessment of trade-offs between achieving equitable energy and climate futures. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental social sciences Integrated Assessment Model climate equity energy equity sustainable development climate justice net-zero emissions modelled pathways scenarios Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Since the 5th Assessment Report of the IPCC, the process of building climate scenarios has become intrinsically tied to the process of building socio-economic scenarios (van de Ven et al, 2025). The emissions trajectories used by WG-I to estimate associated warming levels and earth system impacts are produced using Integrated Assessment Models (IAMs). These models are meant to provide integrated simulations of the physical, economic, and energy systems and typically do so through the integration of economic models, energy optimisation models, and vegetation models (Parson and Fisher-Vanden, 1997 ; Skea et al., 2021 ). The emissions trajectories produced by the IAMs represent outcomes that depend strongly on a of a range of underlying assumptions (Ackerman et al, 2009 ). The regional distribution of the mitigation burden, the potential changes in the pace of economic growth and development across regions, the achievement of sustainable development goals, ensuring food security and well-being, among others, represent the range of possibilities for the future that are put together in some coherent mathematical form to arrive at consequent energy and emissions projections. It is possible to arrive at the same emissions trajectory using different combinations of the underlying assumptions (IPCC, 2022 ). For example, the same emissions trajectory can be achieved through assumptions of continuing global inequality as well as through the elimination of global inequality, or through different assumptions about how the global burden of mitigation is distributed across regions (Ranjan et al, 2024 ; Kanitkar and Jayaraman, 2025 ). The projections of socio-economic futures that underlie the emissions trajectories are therefore not sacrosanct and robust science requires the exploration of a broad range of possibilities that may result in similar emissions outcomes. The scenarios assessed in the 6th Assessment Report (AR6) of the IPCC were produced using IAMs. Five socio-economic baselines called Shared Socio-Economic Pathways or SSPs were constructed and used extensively in the AR6 to explore mitigation policies. The stated purpose of these SSPs was to combine “ pathways of future radiative forcing and their associated climate changes with alternative pathways of socioeconomic development in order to carry out research on climate change impacts, adaptation, and mitigation ”. These pathways were meant to represent “plausible” socio-economic trends for economic, social, and ecological developments over this century, in the absence of climate policies (O’Neill et al, 2014; Riahi et al, 2017; Van Vuuren et al, 2017). The proponents of the SSPs argue that this approach allows for the exploration of scenarios that do not just cover a wide range of emissions outcomes, but also a wide range of socio-economic trends and policy outcomes (ibid). However, the literature produced since AR6 shows that the SSPs and the modelling approach used to quantify them, have narrowed rather than broadened the exploration of possible futures (Rosen, 2021 ; Kanitkar et al, 2024 ; Wood et al, 2024 ). The SSPs as well as the scenarios literature in the climate change domain focus on meeting the temperature targets of the Paris Agreement and thereby mitigation is foregrounded as a primary policy concern across all scenarios. However, developing countries have always maintained, and the framing of the UNFCCC also makes it clear, that for countries that have contributed little to historical greenhouse gas emissions and have significant development deficits, poverty eradication and sustainable development continue to be over-riding priorities (UNFCCC, 1992). Further, the SSP based scenarios have narrowed the possible futures they explore to those that perpetuate a whole range of global inequalities across several key variables, including continuing income disparities, significantly lower levels of energy access in the global South, and the perpetuation of food insecurity and increasing risk of hunger (Kanitkar et al 2024 ; Jaiswal et al 2024 ). There is no storyline that projects the achievement of even the basic Sustainable Development Goals (SDGs) of zero hunger and poverty eradication (van Soest et al, 2019 ; Jaiswal et al, 2024 ). The narrow range of socio-economic outcomes as well as well as the unequal distribution of the mitigation burden between developed and developing countries in the scenarios of AR6, is a result of both the assumptions embedded in the SSPs and the approach to modelling that the IAMs use (Kanitkar 2024; Li et al, 2024; Stanton et al, 2009 ). It is therefore necessary to rethink and develop new approaches to scenario building that are not constrained by the economic approaches and methodological choices inherently embedded in the current IAMs. In this context we present a new approach to modelling and scenario building in this paper, developed further from the proposed new framework in Ranjan et al ( 2024 ). We demonstrate the use of a user-interface-enabled analytical model, Model for Energy Equity and Climate Compatibility_Version.1 (MEECC_V.1), to facilitate the construction of a range of scenarios to operationalize climate-compatibility, equity, and feasibility. This model is published under the Creative Commons Licence; the Python Code is available at https://doi.org/10.5281/zenodo.15926615 and the interactive excel-based model is available on request. In contrast to the IAM models predominantly in vogue today, this model framework is developed at a high-level of generality, involving only three essential variables to track the energy-economy-emissions linkages, without elaborating any of these three components in any level of detail at this stage. Such elaboration will be undertaken later. The three variables that we focus on are energy consumption, energy efficiency and the emissions intensity of energy, all of which are taken at the economy-wide level and not disaggregated by sectors. Low-carbon development is considered through trajectories with progressive improvement in energy efficiency of the economy (i.e., decoupling energy and economic growth) and increase in energy use in developing economies to meet at least minimum developmental thresholds, while climate constraints are implemented through cumulative emissions limits. The model enables the comparison of futures with and without convergence and reduction of inter-country inequalities in these key variables, since it is at this level that the gaps in the current IAMs are most starkly visible, despite their considerable complexity and granularity. We broadly follow the methodological approach proposed in Ranjan et al ( 2024 ), with some key differences. These differences are a result of making the model more user interactive as well as the introduction of elements that allow for a wider scope in scenario generation. Figure 1 is a graphical summary of the methodology for the Model for Energy Equity and Climate Compatibility_Version.1, or MEECC_V.1 for short. The user interface (UI) provides the possibility of building a wide range of socio-economic and energy scenarios for the future. Model results can be used for assessing global outcomes and their regional distribution, as well as for assessing outcomes for individual countries given a set of requirements and assumptions. 2. Results Figure 2 shows the classification of countries into five groups based on the use of all six methods of classification currently in the model. Countries can belong to different groups based on the approach used and also based on the weighting schema selected by the user within a particular approach. This allows for a wider scope for comparison across scenarios. In the illustrative scenarios shown in this paper, we use the UMAP-k-means clustering approach to classify countries in five groups. The methods section of this paper discusses the methods used for country classification in detail. 2.1 Scenario Development We demonstrate the model here through the development of three broad alternatives in terms of equity that explore two basic goals – energy equity and climate equity. Socioeconomic Alternative-1 (SEA-1) is the alternative that explores meeting both energy and climate equity. In SEA1, energy equity is operationalised through the convergence of per capita primary energy to 75 GJ/person/year by 2050. In this scenario, GDP growth is assumed to occur across all regions. Capital scarce countries at lower levels of GDP in 2019 are assumed to grow at faster rates. However, despite this, some least developed countries, especially in groups 4 and 5 do not reach the threshold level of $ 28,000 per person per year that is necessary for the achievement of developmental goals. Therefore, it is assumed that higher GDP growth will be made possible in these countries, enabling the achievement of at least this threshold by 2050. The routes through which this can be made possible can be explored exogenously. Climate equity is operationalised in this scenario through two assumptions. First, the CO 2 -FFI emissions peak at different times for each country depending on its historical responsibility, i.e. the distance between the fair share and actual emissions of that country between 1850 and 2019. Emissions of countries that have emitted more than their fair share are assumed to have already peaked, or to peak in 2020 itself. Peaking years for other countries are either 2025, 2030, or 2035 based on the degree of difference between their fair and actual shares. Second, each country can access its per capita fair share of the RCB. Taking historical responsibility fully into account would require the consideration of the total global carbon budget and not just the remaining carbon budget (Jayaraman et al, 2012 ). However, this would lead to significant negative emissions for most developed countries in group G1 and G2. Given that the uncertainty of achieving the scale of negative emissions needed and the potential impacts of the same, we do not consider the total carbon budget. However, we use variations in per capita fair share estimate to account for historical responsibility and respective capability as discussed in the previous section. In SEA-1, we consider the per capita fair share weighted by historical cumulative emissions (50% weight) and capability represented by per capita GDP (50% weight). Alternative SEA-2 projects a world with energy convergence, but without climate equity. Energy and GDP assumptions remain the same as SEA-1 in this storyline, however, the RCB is not allocated according to per capita fair shares. It is assumed that current fast/high emitters will continue to emit and appropriate a higher share of the remaining carbon budget. In this scenario, it is projected that countries share of the RCB will be equivalent to their current (2019) share of annual emissions. In alternative SEA-3 (Energy gap and climate inequity) a wide gap remains in primary energy consumption across different development groups even by 2050. By 2050, countries in group G1 either reduce or increase to, and stabilize at 160 GJ/person/year, countries in G2 at 120 GJ, G3 at 75 GJ, G4 at 40 GJ, and in G5 at 25 GJ per person per year. Climate inequity is represented in the same way as in SEA-2. CO2-LULUCF emissions are not varied across these scenarios and are assumed to follow a median path where sinks remain at the levels they are in 2019, and sources reduce to zero by 2050 and remain at zero thereafter. Through these three alternatives, we demonstrate the range of issues that can be explored by using simple analytical models such as the MEECC_V.1. The two key gaps in the scenarios assessed in AR6 were i) the unequal distribution of the mitigation burden between developed and developing countries, violating the principles of equity and CBDR&RC enshrined in the UNFCCC an its Paris Agreement, and ii) the continuation of inequalities in all key developmental variables considered in the models, specifically income and energy consumption. We address both in this paper through the development of the three alternatives described above. 2.2 Global Emissions Outcomes Global outcomes follow expected trends, with sharp reductions required in CO2 emissions to limit warming to well within 2ºC. Table 1 shows the emissions reductions required by 2030, 2035, 2040, and 2050 with respect to emissions in 2020 in each scenario across three temperature targets – a 50% probability of limiting warming to 1.5ºC, a 50% probability of limiting warming to 1.7ºC, and a 67% probability of limiting warming to 2ºC. Table 1 Global Net-CO2 emissions reductions (below 2020 levels) in scenarios SEA-1, SEA-2, and SEA-3 for three temperature targets – 50% probability of limiting warming to 1.5ºC, 50% probability of limiting warming to 1.7ºC, and 67% probability of limiting warming to 2ºC. CO2 Emissions – SEA-1 2020–2030 2020–2035 2020–2040 2020–2050 1.5ºC (50%) -67% -73% -87% -96% 1.7ºC (50%) -37% -50% -67% -82% 2ºC (67%) -23% -35% -49% -71% CO2 Emissions – SEA-2 2020–2030 2020–2035 2020–2040 2020–2050 1.5ºC (50%) -49% -78% -96% -100% 1.7ºC (50%) -22% -38% -53% -80% 2ºC (67%) -10% -20% -41% -60% CO2 Emissions – SEA-3 2020–2030 2020–2035 2020–2040 2020–2050 1.5ºC (50%) -50% -80% -98% -100% 1.7ºC (50%) -23% -39% -54% -81% 2ºC (67%) -12% -21% -38% -61% Across the three scenarios global emissions reductions do not differ too widely for a particular temperature target. To limit warming to 1.5 deg. C, model outcomes indicate a 100% reduction, i.e. reaching net-zero CO2 emissions globally by 2050 irrespective of socio-economic assumptions in each alternative. However, the regional projections that result in these global outcomes vary widely across the three alternatives SEA-1, SEA-2, and SEA-3. 2.3 Results for UNFCCC Groups A comparison between the SEA-1 and SEA-2 alternatives allows us to assess the inter-country distribution of the mitigation burden for different temperature targets with and without climate equity. Since the principle of equitable burden sharing based on common but differentiated responsibilities and capabilities is enshrined in the UNFCCC, we can assess the implications of equitable vs. inequitable allocation of the mitigation burden for the UNFCCC groups, i.e. Annex-I parties to the UNFCCC and non-Annex-I parties to the UNFCCC (UNFCCC, 1992). The projections for primary energy consumption and GDP growth remain the same in both the SEA-1 and SEA-2 alternatives. The global future projected is one of converging energy consumption, with a reduction in energy consumption for those above a specified level and increase for those below this level. GDP growth rates for all groups largely follow historical trends, except in cases where countries do not reach a minimum level of $ 28,000 per person per year, by 2050. In such cases, a higher growth rate is assumed in these countries to achieve the minimum threshold for per capita GDP. Baseline emissions (without any additional mitigation) for Annex-I countries show a declining trend because of reducing energy consumption from 187 GJ/person/year in 2019 to 75 GJ/person/year in 2050 (See Fig. 3 a). For non-Annex-I countries, emissions increase in the baseline as energy consumption increases from 53 GJ/person/year in 2019 to 75 GJ/person/year in 2050 (See Fig. 3 b). In SEA-1 scenario, under all temperature targets, the required reduction in the emissions intensity of energy is higher for Annex-I parties as compared to non-Annex-I parties (See Fig. 4 a). On the other hand, the required reduction in the emissions intensity of energy is higher for non-Annex-I parties in scenario SEA-2, when climate equity is compromised, under all temperature targets (See Fig. 4 b). In the SEA-3 alternative, energy consumption for Annex-I countries reduces only marginally from 187 GJ/person/year in 2019 to 148 GJ/person/year in 2050. The increase for non-Annex-I countries is also marginal, from 53 GJ/person/year in 2019 to 56 GJ/person/year in 2050. Additionally, there is no climate equity implemented in this scenario. Figure 5 shows the near-term (2020–2030) reductions in emission intensity required in scenarios SEA-1 and SEA-3 for Annex-I and non-Annex-I countries for a target of limiting warming to 1.7ºC (50% probability). In SEA-1, the Annex-I group is required to reduce its emissions intensity by 6% per year between 2020 and 2030, whereas the non-Annex-I group is required to reduce it by 3% in the same period. The requirement for the Annex-I group reduces significantly to 2% per year in the SEA-3 scenario despite higher energy consumption in this scenario for this group. On the other hand, for non-Annex-I countries the requirement still remains at about 3% per year as most of the mitigation in this group is in fact achieved through a suppression of energy demand itself. The SEA-3 therefore mimics the IPCC AR6 scenarios closely. 2.4 Results for Developmental Groups It is also possible to assess results by developmental groups. Figure 6 (a) shows the emissions projections for the five development groups (with G1 being the most developed and G5 being the least developed group) under carbon budget constraints for three temperature targets for scenario SEA-1. For the same temperature targets, Fig. 6 (b) shows the emissions projections for all five groups for scenario SEA-2 (energy equity but no climate equity), and Fig. 6 (c) shows the projections for SEA-3 (neither energy nor climate equity). In SEA-2, i.e., when higher energy consumption is ensured but not an equitable share of the carbon budget, the model returns “no solution” for 53 countries, mostly in groups G4, and G5, for a temperature target of 1.5ºC. This is because a minimum gap of 10 years is required between the year of peak emissions the year of net-zero emissions. Given that countries in groups G4 and G5 are typically the ones that have not over-used their fair per capita share of the carbon budget in the past, the model allows for later peak years for these countries, consequently rendering it impossible to reach net-zero quickly thereafter (within 10 years) given the unequal carbon budget allocations in SEA-2. This does not change even if the constraint on LULUCF emissions (i.e., the degree of CO2 removal that is assumed to be possible), is changed to the maximum possible limit currently allowed in the model. Only if the peaking year for countries in these groups is advanced considerably to 2025, the model returns a mathematically feasible solution. Since SEA-2 is the scenario in which countries are expected to reach energy consumption levels of 75 GJ per person/year by 2050, the model outcomes suggest that achieving these levels of primary energy consumption within a very limited carbon space that is allocated to G4 and G5 countries in SEA-2 is not possible unless their emissions are forced to peak immediately. On the other hand, in SEA-1 where these countries get their fair share of the remaining carbon budget, while burdens are still high for the more stringent temperature target of 1.5ºC (50% probability), the model still returns at least a mathematically feasible solution. If neither energy nor climate equity is met, i.e. in SEA-3, constraints of limiting temperature rise to 1.7ºC (50% probability), returns a mathematically feasible solution for most countries, albeit at the cost of a hugely reduced share of the remaining carbon budget for countries in the G4 and G5 groups, which would imply a severe constraint on their development.. Figure 7 shows the comparison of the cumulative emissions in scenarios SEA-1 and SEA-3 for a temperature target of 1.7ºC (50% probability). In contrast, the cumulative emissions of groups G1, G2, and G3 increase in scenario SEA-3 as compared to SEA-1, with the highest expansion for G1, i.e., the most developed countries. Such group-wise analysis allows us to explore the potential trade-offs in meeting multiple goals for countries at various stages of development. 2.5 Illustrative results for country-level analysis It is also possible to explore the range of possibilities for specific countries. Here we demonstrate the outcomes for two countries – Germany and India. With the k-means criteria of clustering used so far, Germany is in development group G1 and India is in group G4. Table 2 shows how these two countries are classified if different classification criteria described in the methods section are used. Table 2 Classification for Germany and India using six different classification criteria Method of classification Group for Germany Group for India Per capita GDP in 2019 G1 G4 Per capita CO2 emissions in 2019 G1 G3 Historical cumulative emissions as compared to fair share G1 G5 Combination of per capita GDP and historical cumulative emissions with 50% weights to each G1 G5 Equal Weighting of all development variables (excluding emissions) G1 G3 UMAP + k-means clustering G1 G4 Irrespective of the classification criteria used, Germany gets classified into group 1. This is true for a large majority of the non-EIT Annex-I countries. On the other hand, based on the classification criteria used, India gets classified in groups G3, G4, or G5, not making it to groups G1 or G2 irrespective of the clustering method used. For the results in the rest of this section, we take the classification from the k-means clustering method, with Germany in G1 and India in G4. Figure 8 shows the per capita energy and GDP trends in scenario SEA-1 and SEA-3 for both these countries. The main difference between the two countries is observed in the energy projections for scenarios SEA-1 and SEA-3 where primary energy consumption converges in the former and in the latter the differences remain significant throughout the century. The implications of equitable and unequitable sharing of the carbon budget for the same energy and economic growth projections under different temperature targets can be estimated in our model. Table 3 shows the range of net-zero years under a range of temperature and peak year constraints for scenarios SEA-1, SEA-2, and SEA-3 for India. For scenario SEA-1, the option for late peaking (peak emissions in 2040) does not yield a mathematically feasible solution for India for the more stringent mitigation target of 1.5 deg. C (50% probability). However, for all other temperature target, irrespective of peaking year, the model provides the results demonstrating the range of possibilities if the carbon budget is allocated equitably. On the other hand, under both the SEA-2 and SEA-3 scenarios, where climate equity is not ensured, meeting the 1.5 deg. C (50% target) is impossible for India even within the loosely defined constraints in the model. Even with significantly constrained primary energy consumption levels in scenario SEA-3 (40 GJ/person) as compared to SEA-2 (75 GJ/person), the mitigation burden imposed on India due to a higher share of the carbon budget being consumed by countries in other groups, makes meeting both the energy and climate targets impossible. Even the 1.7 deg. C (50%) target can only be met with relatively early peaking of emissions and with constraining energy consumption to levels far below those required for sustainable development. This result underscores the significant importance of climate equity. Table 3 Net-Zero years in Scenarios SEA-1, SEA-2, and SEA-3 for India under a range of temperature and peaking year constraints Peak Year Net-Zero Year – SEA-1 Net-Zero Year- SEA-2 Net-Zero Year- SEA-3 1.5ºC (50%)-Late peaking 2040 NS NS NS 1.5ºC (50%)-Medium peaking 2035 2044 NS NS 1.5ºC (50%)-Early peaking 2030 2053 NS NS 1.5ºC (50%)-Immediate peaking 2025 2065 NS NS 1.7ºC (50%)-Late peaking 2040 2058 NS NS 1.7ºC (50%)-Medium peaking 2035 2068 NS NS 1.7ºC (50%)-Early peaking 2030 2082 NS 2042 1.7ºC (50%)-Immediate peaking 2025 2101 2045 2048 2ºC (67%)-Late peaking-2 2040 2074 NS NS 2ºC (67%)-Medium peaking 2035 2088 NS 2046 2ºC (67%)-Early peaking 2030 2107 2046 2053 2ºC (67%)-Immediate peaking 2025 2133 2057 2062 Emissions in Germany have already peaked. So here, we demonstrate the required net-zero years for Germany under different temperature targets without altering the peaking year, which for the purpose of the model is 2020. As shown in Table 4 . Table 4 Net-Zero years in Scenarios SEA-1, SEA-2, and SEA-3 for Germany under a range of temperature constraints Net-Zero Year – SEA-1 Net-Zero Year – SEA-2 Net-Zero Year – SEA-3 1.5ºC (50%) 2032 2042 2041 1.7ºC (50%) 2044 2060 2059 2ºC (67%) 2053 2075 2074 Even if it consumes a disproportionate share of the remaining carbon budget (i.e. a share proportional to its current annual share of CO2-FFI emissions) as in SEA-2, Germany will have to reach net-zero by 2042, about 8 years before its declared net-zero year, to be aligned with the 1.5 deg. C target (50% probability). This is despite significant reduction in energy consumption in Germany in this scenario, i.e. from 160 GJ to about 75 GJ. Access to only the fair share of its carbon budget for the 1.5 deg. C target (50% probability) would require Germany to advance its net-zero year to 2032. Figures 9 and 10 show the net CO2 emissions for India and Germany for all three scenarios (SEA1, SEA-2, and SEA-3) for temperature targets of 1.7 and 2ºC. The scenarios show the range of projected emissions with and without climate and energy equity. A similar analysis can be undertaken for all countries included in the model. 3. Discussion An Integrated Assessment Model is defined by the IPCC as “ a model that combines representations of environmental processes, economic dynamics, and policy interventions to explore sustainable development pathways and climate mitigation strategies ” (IPCC, 2014 ). Other definitions in the literature are similar (Nordhaus, 1992 ; van Vuuren et al., 2011). None of these definitions suggest that the use of economic models with a particular underlying economic logic, or the use of cost-optimisation techniques in energy models is a necessary requirement in the construction of models to qualify under this definition as an IAM. However, this has almost become the norm, with most, if not all, existing IAMs using the same underlying logic and approach. In the new model (MEECC_V.1) presented in this paper, we have proposed an alternative framework for integrated assessment. We integrate economic, energy, and environmental aspects, but in a manner that differs significantly from the current set of IAMs We depart from the usual approach used in IAMs in four ways: i) we use a development-based classification of regions and not a geographical classification, ii) we do not use the general equilibrium framework for modelling the socio-economic dimensions, but use overall economy-wide metrics, iii) we do not use carbon prices and global least-cost optimisation as the framework, but use analytical approaches to explore a range of energy futures, iv) we do not assess emissions outcomes as a result of optimising energy consumption and production optimisation, but instead use a range of allocation approaches to distribute the global carbon budget across countries and/or development groups. We do of course have a relatively simple construction of the energy and economy sector, but then we do not miss the wood of energy access and climate equity for the trees of granularity and complexity that are intrinsic to the IPCC AR6 IAMs. Some of the IAMs also provide results for fossil fuel transitions that are based on optimisation done at the global level. However, each country implements energy policies at the national level. These are not based on some global carbon price considerations, but through planning exercises for the short, medium and long-term considering their own national circumstances and economic and policy constraints. Unlike the downscaling methods for national level considerations in the current IAMs, the results from the MEECC can be used by stakeholders to determine the constraints that can be used to design national energy transition strategies, by using modelling techniques of their choice within the scope of global equity considerations. Our results demonstrate the potential use of the model in assessing trade-offs between achieving equity in energy futures and equity in sharing of the mitigation burden. In scenarios which meet both energy and climate equity, the burden on Annex-I countries, or countries in the high development groups (G1 and G2) are significantly higher. On the other hand, most countries at low levels of development cannot achieve higher levels of energy consumption to meet developmental needs, in a manner that is also compatible with the global goals of the Paris Agreement, unless they can use their fair share of the carbon budget. The model can also allow users to explore a range of scenarios that meet energy, climate, and developmental targets and the trade-offs between these. This is important in the context of setting targets through the Nationally Determined Contributions (NDCs), or evaluating targets and contributions on the basis of equity. Article 14 of the Paris Agreement requires that “ Parties [to the Agreement] shall periodically take stock of the implementation of this Agreement to assess the collective progress towards achieving the purpose of this Agreement and its long-term goals (referred to as the “global stocktake”) ”. The global stocktake is to be undertaken “ in the light of equity and the best available science ”. Analytical models such as the MEECC can allow for such an analysis to facilitate i) ex ante analysis to enable the setting of fair and scientific targets and ii) ex post assessment of targets on the basis of equity and the best available science. We note that, in our view, there is no current IAM model to provide robust assessments of the equity of various country NDCs in alignment with the principles and values of the UNFCCC and its Paris Agreement. The first version of the MEECC that we present here will be available in the public domain and be periodically updated and improved based on feedback from a wide range of stakeholders. We also hope to be able to pursue the coupling of this model with more granular national models to explore the relationship between constraints that operate at the national level and global considerations of alignment with the temperature goals of the Paris Agreement on the basis of equity and justice. 4. Methods Each step of model creation is explained in the following sub-sections. We also refer to specific select features of the user interface alongside these explanations. 4.1 Country Classification Unlike current IAMs, where regions, with the exception of the OECD group of countries, are classified by geography, the MEECC_V.1 uses development-based classification for regions. The model provides users with a choice of parameters based on which regions can be classified. Ranjan et al ( 2024 ) use principal-component analysis and k-means clustering to classify countries into four development categories. In MEECC_V.1, we classify counties into five groups as this provides a more consistent and uniform distribution of countries across development groups. We use multiple approaches for country classification in the MEECC_V.1 and allow the user to choose a method based on the type of analysis they want to undertake. Currently, six different ways to classify countries into development groups are included in the model, summarized in Table 5 . Table 5 Approaches in the MEECC_V.1 for classifying countries into five developmental groups. Basis for Classification Description 1. Income Countries are classified based on per capita GDP for year 2019 2. Per capita emissions Countries are classified based on per capita CO2-FFI emissions in year 2019 3. Historical Responsibility Based Countries are classified based on their historical responsibility for past cumulative emissions. This is determined by the difference between their actual emissions and their per capita fair share of the carbon budget between 1850 and 2019 4. CBDR&RC Countries are classified based on a combination of per capita GDP in 2019 (representing current capability) and the difference between their actual emissions and their fair share of the carbon budget between 1850 and 2019 (representing historical responsibility). A combination of weights can be used to assign relative importance to each of these criteria following Holz et. al ( 2019 ) 5. Based on 16 development indicators Weights can be assigned by users to 16 development variables for income, health, education, infrastructure, energy, and emissions, based on which countries are then classified into development groups. k-means clustering Uniform Manifold Approximation and Projection (UMAP), and clustering via the K-Means algorithm. See SI for details. The development variables used in the last two approaches are summarized in Table 6 . The baseline year for data is 2019 in MEECC_V.1. While historical responsibility for climate change mitigation can be operationalised to some extent by allocating future mitigation burdens based on past emissions and current capacities, its full extent cannot be captured with ever advancing base years in models. This has been true for all models that have been used as inputs to the IPCC assessment process so far, where every generation of models begins from the most recent base year. We hope to address this issue in the next version of the model. Table 6 Variables for health, education, economy, infrastructure, energy, and emissions used for country classification and model simulations. Dimension Health Education Economy and Infrastructure Energy Emissions Variable Life Expectancy - LEX [yrs] Mean Years of Schooling - MYS [yrs] Deaths due to unsafe sanitation - DUS [deaths per 100,000 people] Per capita primary energy consumption - PPE [GJ-person − 1 ] Per capita CO2-FFI - PCO2-FFI [tCO2-person − 1 ] Infant Mortality - IMR [deaths per 1000 live births] Maternal Mortality Ration - MMR [women's deaths per 100,000 live births] Access to Internet - IAC [% of people who used the internet in last 3 months] Share of the urban population living in slums - PLS [% of urban population] Access to Electricity - ELEC [% of households] Distance from fair share of cumulative CO2-FFI - CCO2-FF1_DFS [GtCO2] Share of deaths attributable to air pollution - DAP [% of total deaths] Daily Calorific Intake - DAC [kcal-person − 1 -day − 1 ] Death rate from unsafe water sources - DUW [Deaths per 100,000 people] Access to Clean Cooking Fuels - CCF [% of households] 4.2 Growth Projections and Macro-Economic Constraints The next step is to specify the projections for economic growth. In most existing IAMs, GDP growth is estimated using macro-economic models, most of which are based on neoclassical economic assumptions. These macro-economic models may be external, to the energy, emissions and land-use calculations, with the GDP projections being exogenous or they may also be internal to the overall model as a component, with the GDP projections being endogenous. For instance, Cobb-Douglas production functions or their variants are often part of these models, and determine total economic output for regions based on assumptions about total factor productivity and factor substitutability for the regions. This applies especially to the IAMs in which GDP is determined endogenously such as the REMIND-MAgPIE model (Bauer et al, 2011 ). In such models, ostensibly to allow for consistency with ‘past trends’ in incomes, or to ensure that incomes across regions do not converge at rates that are ‘unsupportable’ in reality, the distribution of incomes across regions is frozen using Negishi weights (Stanton, 2009). Indeed, modelers themselves have claimed that in the absence of such weighting, the problem of income distribution across regions overwhelms the problem of climate change mitigation (Stanton, 2011 ). The principle of Pareto-optimality, which is applied as the optimization principle in some models, essentially negates any possibility of distributive justice. This is especially true of distributive climate justice to compensate for historical responsibility for cumulative emissions as well as insufficient emissions reductions that do not adhere to the climate convention’s principle that developed countries should take the lead in climate action. Counter-arguments that welfare and distributive goals can be achieved “outside the models” (Pachauri et al, 2022 ), such as transfer of resources from collections of carbon tax and carbon credit auctions, are problematically circular, as the models themselves are constructed by explicitly ruling out such transfers. Some other models construct regional GDP projections quasi-endogenously, such as in the MESSAGE_GLOBIOM model for example. While the energy sector model in MESSAGE allows for the endogenous re-calculation of GDP based on a range of energy-supply outcomes resulting from least-cost optimisation, this newly calculated GDP is vetted against an exogenously supplied GDP trend and adjusted (“calibrated” is the term used) if it deviates too much from the latter (Krey et al, 2020). The exogenous GDP trend is in turn produced using macro-economic algorithms that are based on similar neoclassical assumptions discussed above (Huppman et al, 2019). Thus, despite the elaborate computational setup to endogenously calculate GDP in sync with energy use and the emissions from different energy sources in various sectors, the end result is arbitrarily constrained by externally determined GDP growth estimates. As a consequence, we do not use such models for GDP projections. In MEECC_V.1 we will simply specify GDP as an empirical indicator of economic activity, focusing on trajectories of GDP growth of countries to focus on inter-country comparisons related to inequalities between countries. Within-country income distribution and equality can be better analysed with more detailed national and sub-national models instead of through global models, or perhaps even be incorporated in this framework, but these considerations are not part of this model. Our strategy does not imply that we argue for GDP itself is a complete measure of well-being. Obviously, outcomes for well-being and development would also depend on the way in which wealth is created and distributed, not all of which is captured by a single measure such as GDP. Following Ranjan et al ( 2024 ), we construct multiple trajectories for GDP growth. Users can select different GDP trajectories from a range of potential economic growth scenarios constructed for each country group. This list is non-exhaustive and can be further expanded. In the current version of the model, we have three broad growth scenarios and 7 variations across each of these. These are summarized in Table 7 . Table 7 Economic Growth Trajectories and Scenario Sets Scenario Group Scenario Name Description 1. Uniform growth scenarios GDP growth is seen in for all development groups with rates of growth progressively increasing from G1 to G5. Post 2050, growth rates of G1, G2, G3, G4, G5 are 0%, 0.5%, 1%, 1.5% and 2% for all scenarios. GDP_UHigh High growth for all groups resulting in higher global GDP by 2050.GDP of G1, G2, G3, G4, G5 grows at 2%, 4%, 5%, 6% and 7% respectively. GDP_UMed Medium growth for all groups resulting in moderately high global GDP by 2050.GDP of G1, G2, G3, G4, G5 grows at 1.5%, 3%, 4%, 5%, and 6% respectively GDP_ULow Low growth for all groups resulting in lower global GDP by 2050. GDP of G1, G2, G3, G4, G5 grows at 1%, 2.5%, 3.5%, 4.5% and 5% respectively 2. Divergent growth scenarios Reducing economic growth for groups G1 and/or G2. Post-2050, growth rates of G1, G2, G3, G4, G5 are 0%, 0.5%, 1%, 1.5% and 2% for all scenarios. GDP_DG1 GDP of G1, G2, G3, G4, G5 changes at 0%, 3%, 4%,5% and 6% respectively GDP_DG2 GDP of G1, G2, G3, G4, G5 grows at -1%, 2%, 4%, 5%, and 6% respectively 3. Threshold scenarios A minimum threshold of $ 28,000 for per capita GDP is assumed for all countries. Post-2050, growth rates of G1, G2, G3, G4, G5 are 0%, 0.5%, 1%, 1.5% and 2% for all scenarios. GDP_UMod-TS1 GDP growth same as GDP_UMed scenario except for countries which do not reach $ 28,000 per capita GDP till 2050. Higher growth assumed for these countries to achieve threshold GDP_DG2-TS2 GDP growth same as GDP_DG2 scenario except for countries which do not reach $ 28,000 per capita GDP till 2050. Higher growth assumed for these countries to achieve threshold The approach used in the MEECC_V.1 allows for a range of possible GDP indexed futures. The degree of decoupling between GDP and energy can then be assessed depending on the potential economic and energy growth that is projected. This also allows us to assess trade-offs between climate action and energy use in a much more transparent manner than can be done using the general equilibrium models. Full convergence in GDP is only possible by assuming either slower or reducing economic activity levels in richer countries. While it is technically possible to model such scenarios in the MEECC_V.1, we do not emphasize these scenarios in our results. The implications of such shifts in GDP growth, especially for poorer populations even within rich economies, are highly contested. Even where reducing levels of economic activity is possible – for example in high income economies with capital saturation, ensuring that such policies are based on principles of distributive justice rather than on further exacerbating income inequalities would require significant transformation in political and economic perspectives. Since global models, either the current IAMs or the MEECC_V.1 do not (and to an extent also cannot) do this, we refrain from using such scenarios in our results, even where they have been argued to be applicable, i.e., for rich economies (Victor, 2012 ; Lenzen et al, 2022 ; Li et al, 2024). Instead, we project a range of values for future GDP growth with higher rates of economic growth in developing countries, enabling scenarios with faster reduction of the gaps between groups of countries. Additionally, in some scenarios, we set a minimum threshold of per capita GDP which must be achieved at least by 2050 in all countries, to facilitate the achievement of well-being levels across multiple development indicators. Currently we set that threshold at $ 28,000/person/yr following Ranjan et al ( 2024 ). In future versions of the model, this can be developed further. 4.3 Energy Consumption In the current set of IAMs, primary energy consumption estimates are closely related to or based on projections for GDP and other economic variables. As the demand sectors are disaggregated differently with varying assumptions of demand-side behaviour across models, estimates of final energy consumption can be different across scenarios. However, the strong dependence on economic variables in current IAMs leads to very similar outcomes for primary energy consumption across scenarios (Kanitkar et al, 2024 ). In contrast, in the MEECC_V.1, users can choose GDP and energy pathways independently for building any scenario, and the resultant changes in the energy-GDP relationship represent the degree of decoupling that would be required to achieve the desired outcomes. While it is true that there is a strong correlation between GDP and energy consumption historically, this relationship would need to change (IPCC, 2022 ) in the face of the increasing need to address global warming. However, this change would have an impact on the nature of economic growth in a country given that production processes for conventional and non-conventional energy vary widely and their supply chains are distributed very differently across the world (Khan et al, 2021 ). In the current version of our model, the user can choose 8 potential pathways from two scenario sets. This list can be expanded further in subsequent versions of the model. There are a range of potential energy thresholds corresponding to development indicators that have been explored in the literature (Ranjan and Kanitkar 2025 ). In our model, we use these estimates from the literature to construct scenarios that either project energy convergence or divergence across the five development groups. This is similar to the methodology followed in Ranjan et al ( 2024 ). In the energy convergence scenario-set, users can choose energy conversion thresholds ranging from 60 to 95 GJ. In this scenario-set, countries or development groups above these thresholds are assumed to move towards these thresholds by the target year and those below these are projected to increase and then stabilize at these threshold levels. In the scenario sets with no convergence, the target energy consumption level for each development group is input separately. Table 8 summarizes the scenario sets that users can choose from. Table 8 Scenario sets for primary energy consumption in MEECC_V.1 Scenario Group Scenario Name Description Convergence scenarios: Energy convergence to a particular level by 2050, eventual convergence to 70 GJ for all by 2100 ECOV_GlAvg75 All groups converge at 75 GJ/p by 2050 and remain at this level beyond 2050 ECOV_QPT60 All groups converge at 60 GJ/p by 2050 and remain at this level beyond 2050 ECOV_QPT70 All groups converge at 70 GJ/p by 2050 and remain at this level beyond 2050 ECOV_QPT80 All groups converge at 80 GJ/p by 2050 and remain at this level beyond 2050 ECOV_QPT90 All groups converge at 90 GJ/p by 2050 and remain at this level beyond 2050 ECOV_QPT95 All groups converge at 95 GJ/p by 2050 and remain at this level beyond 2050 Divergence scenarios: Energy consumption different for different groups, eventual convergence to 70 GJ for all by 2100 EDIV_High High divergence: G1 reaches or reduces to 200 GJ by 2050 and then remains at this level; G2 reaches or reduces to 160 GJ and remains at this level; G3 reaches or reduces to 55 GJ and then remains at this level; G4 reaches or reduces to 30 GJ and then remains at this level. G5 reaches or reduces to 15 GJ and then remains at this level EDIV_Med Medium divergence: G1 reaches or reduces to 160 GJ by 2050 and then remains at this level; G2 reaches or reduces to 120 GJ and remains at this level; G3 reaches or reduces to 75 GJ and then remains at this level; G4 reaches or reduces to 40 GJ and then remains at this level. G5 reaches or reduces to 25 GJ and then remains at this level The user can also select the rate at which the relationship between primary and final energy changes across the modelling time-period. In the current version of the model, this is assumed to be the same for all development groups given that, in the first instance, energy efficiency improvements or shifts in production processes can work in similar fashion wherever they are adopted. 4.4 CO 2 emissions CO 2 emissions from fossil fuel combustion and industrial production (CO2-FFI) and CO 2 emissions from land use and land use change and forestry (CO2-LULUCF) are considered separately in the MEECC_V.1. This version of the MEECC does not include non-CO2 GHG emissions. This will be included in later stages as other elements of human and climate systems interactions are included. CO 2 -LULUCF Estimates of CO 2 -LULUCF emissions have a much higher degree of uncertainty – both for historical and current emissions (Lamb et al, 2021). Additionally, the degree and rate at which CO 2 -LULUCF emissions can change is also highly uncertain. Large-scale land-based mitigation has been shown to have serious implications for food security (Fujimori et al, 2019 ; Fujimori et al, 2022 ; Jaiswal et al, 2024 ). Additionally, the actual scale of possible carbon dioxide removal (CDR) from other sources is as yet uncertain, and the effectiveness of some of the methods in ensuring long-term and sustained CO 2 removal is also contested (Anderson and Peters, 2016 ; Dooley and Kartha, 2018 ). Therefore, while the model does include CO 2 -LULUCF emissions, we have, in the current version, three fixed scenarios through which we project changes in these emissions for the future (See Table 9 ). Table 9 CO2-LULUCF Scenarios in in MEECC_V.1 Scenario Name Description LULUCF_BY Annual CO2-LULUCF emissions remain the same as base year, i.e. sources remain sources and sinks remain sinks LULUCF_PartShift Sinks remain the same as 2019 and sources become zero-emission by 2050 and remain at zero thereafter LULUCF_Shift All regions with net-positive CO 2 -LULUCF emissions reduce to zero by 2050 and a maximum of -0.005 GtCO2 by 2100. Those below − 0.005 GtCO2, in 2019 remain at 2019 levels. Global CO 2 -LULUCF emissions in 2019 are estimated to be about 4.53 GtCO 2 (Ref-PRIMAP database). The land sector is therefore currently a source of GHG emissions at the global level. In the LULUCF_BY scenario, CO 2 -LULUCF emissions are assumed to remain at this level through the century, with a cumulative emission of 82 GtCO2 globally from LULUCF between 2020 and 2100. In the LULUCF_PartShift scenario cumulative CO 2 -LULUCF emissions between 2020 and 2100 are 42 GtCO2, with annual emissions in 2050 reaching − 0.42 GtCO 2 and in the LULUCF_Shift scenario cumulative emissions between 2020 and 2100 are 25.53 GtCO 2 with annual emissions in 2100 reaching − 1.72 GtCO 2 . The total negative emissions between 2020 and 2100 range between 34–57 GtCO2 across all scenarios, which is very conservative as compared to carbon-dioxide removal projected by scenarios assessed in AR6 (Kanitkar et al, 2024 ; Jaiswal et al, 2024 ). CO 2 -FFI For projecting the potential and required behaviour of CO2-FFI emissions, three inputs are required to the model: i) choice of temperature target with a corresponding probability of achieving that target, ii) the principle based on which the remaining carbon budget will be distributed between countries, and iii) the peaking year for countries in each development category. The emissions intensity of energy improves with improving technology, increases in scales of production, and shifts in technology use. This may happen also without any specific policies targeted towards climate change mitigation, as fuel use becomes more efficient and due to technological advances that are driven by other factors However, it is difficult to attribute the rate of such improvements in the absence of targeted policies, as in most countries targeted policies for efficiency improvement already exist and have existed for some time. In the current version of the MEECC_V.1 we assume that the level of emissions intensity of energy for each country remains at 2019 levels in the baseline projections. The required rate of change in this indicator is then estimated under different temperature scenarios. In the MEECC_V.1, scenarios can be constructed for different temperature targets, which we currently fix to be 1.5ºC, 1.7ºC, and 2ºC, with corresponding probabilities of either 50% or 67% of limiting warming to these levels. The corresponding carbon budgets, i.e. cumulative CO 2 emissions from 2020 till the point of global net-zero emission for these temperature targets are available from IPCC (2021). These carbon budget values assume certain trajectories of non-CO 2 GHG emissions and the quantum of the remaining carbon budget that will actually be available depends on these assumptions. Currently we assume that these assumptions for non-CO 2 GHG emissions will hold as we do not model non-CO 2 GHG emissions in the MEECC_V.1. We recognize that the behaviour of non-CO 2 GHG emissions is also policy dependent and that the level of non-CO 2 GHG emissions will impact the distribution of the CO 2 budget across countries, and we hope to address this in subsequent versions of the model. The next step is to choose the principle through which the remaining carbon budget (RCB) corresponding to a temperature target will be distributed between regions. In the MEECC_V.1, this is implemented through five scenarios (See Table 9 ). The shares of the RCB accruing to each group as a result of each of these allocation rules is shown in Table 10 . As the difference between the equity-based allocation rules is small, we show a comparison of the two broad approaches – considering or ignoring climate equity. Table 9 Distribution of the Remaining Carbon Budget (RCB) between countries Scenario Group Scenario Name Description Equity Based PCFS_GF Per capita fair share of the RCB based on 2019 population, not accounting for historical responsibility or respective capability PCFS_Hist Per capita fair share of the RCB based on 2019 population, weighted by historical responsibility - weighted by emissions between 1850 and 2019 PCFS_Cap Per capita fair share of the RCB based on 2019 population, weighted by capability - weighted by per capita GDP in 2019 PCFS_HistCap Per capita fair share of the RCB based on 2019 population, weighted by responsibility and capability - weighted by per capita GDP in 2019 and historical emissions between 1850 and 2019 Inequitable grandfathering PCUS_CurrAnn Per capita share of the RCB same as annual share of current (2019) emissions Table 10 Share of the Remaining Carbon Budget corresponding to user choice for allocation rule Equity based allocation (PCFS_HistCap) Inequitable grandfathering G1 13.2% 29.9% G2 12.9% 16.0% G3 54.3% 48.0% G4 11.3% 2.8% G5 8.4% 3.3% Since the MEECC_V.1 is not an optimisation model, we need to define one additional parameter to be able to construct an emissions trajectory for the future. This parameter is the year at which net-CO 2 emissions will peak in each country or region. We allow for two possible routes for peak-year selection: i) the peaking year for each group can be manually selected by users for each development group or ii) the peaking year can be estimated based on the difference between actual share of past cumulative emissions and fair share. The first method is useful when one wants to explore the possibilities for a particular country given a range of peaking years. The second method operationalises decisions of the UNFCCC that have been restated in multiple decisions since the Paris Agreement that peaking years for emissions will differ for developed and developing countries taking into consideration the latter’s priorities for poverty eradication and sustainable development (UNFCCC, 2023). Eq. ( 1 ), is used to estimate the year of net-zero emissions for each country. $$\:{NZy}_{i}=\frac{2\times\:{CES}_{i}}{{E}_{i}^{{PY}_{i}}}+BY$$ 1 where subscript i indexes countries, NZy i is the year of net-zero emissions, CES i , the cumulative emissions between year of peak and net-zero emissions (i.e. the share of the carbon budget from 2020 onwards after subtracting the cumulative emissions between 2020 and the peak year), E i PYi , the annual emissions in the peak year, and BY , the base year which is 2020 in this case. Linear reductions are assumed between the year of peak and net-zero emissions. The emissions pathway is assumed to follow the baseline trajectory till the time of peak emissions, after which emissions reductions begin. Given that there is almost no carbon budget left for the more stringent temperature target of 1.5 deg. C, the model forces drastic reductions in emissions beyond the peaking year in this case to maintain cumulative emissions within the allocated share of the carbon budget. Such drastic reductions are highly unlikely to be feasible technologically, economically, and politically. The gap between the year of peak emissions and net-zero emissions can vary widely. Since no major economy has as yet achieved net-zero emissions, the actual number of years between peak and net-zero emissions can only be speculated upon. For the EU27, for example – CO2 emissions peaked around 1990 and the target year for net-zero emissions has been pledged to be 2050, indicating a gap of 60 years between peak and net-zero emissions allowing for a gradual transition in fossil fuel use, not in keeping with the rhetoric of a climate emergency. For the US, CO2 emissions peaked around 2005. Before the US withdrawal from the Paris Agreement, it had announced its intension to reach net-zero emissions by 2050. This still allowed for a gap of 45 years between the peak and net-zero emissions. China has announced that its emissions will peak before 2030 to reach net-zero by 2060, which is a gap of about 30 years between peak and net-zero emissions. India’s emissions have not yet peaked, nor are they likely to peak before 2040 or even later, given the significantly lower levels of current per capita emissions, the continuing rising demand for energy, and the lack of domestically available low-carbon alternative or ‘transition fuels’ for coal-based energy in the near term (Kanitkar, 2021 ; Srikanth and Bhatt, 2023 ). Nevertheless, India has announced a net-zero target year of 2070, which means the gaps between peak and net-zero emissions will at most be 35 years, and likely much lesser. The feasibility of being able to achieve net zero emissions under constraints of fair carbon shares depends on a range of factors including, inter alia, levels at which emissions peak, availability of technology, financial capacity and flexibility, levels of energy and other developmental deficits, and political will. Most models address this issue through a mathematical fix of imposing some constraints on the rate of feasible emissions reduction, or the degree of emissions uptake by natural or anthropogenically enhanced carbon sinks, and so on. These constraints are subjective in nature, even though they are not explicitly stated in the published literature, except in some post-facto assessments of these model assumptions (Semieniuk et al, 2021 ; Muttit et al, 2023). Being able to explore the behaviour of emissions under various scenarios can however allow countries to better assess the implications of targets they set for themselves in the context of equitable or inequitable global climate action. Assessments of feasibility can then be carried out at the country level to determine which scenarios are more feasible and/or more equitable than others. In the current version of the MEECC_V.1, the lowest possible gap between peak and net-zero emissions is 10 years and the highest gap is 50 years. Declarations 5. Data Availability All baseline data for decision variables is available at https://doi.org/10.5281/zenodo.15926615 6. Code Availability Python code and dash board information is available at https://doi.org/10.5281/zenodo.15926615 7. Acknowledgements This study has not received any external funding. 8. Author Contributions The first author was responsible for creating the first version of the model, including the conceptualisation, data collection, analysis, and coding, and also writing the first draft of the paper and creating the figures and tables. The second author was responsible for conceptualising the model, data analysis, and writing and editing the paper. The third author was responsible for modelling the database and user interface in python. 9. Competing Interests The authors have no competing interests to declare. References Ackerman, F., DeCanio, S. J., Howarth, R. B., & Sheeran, K. (2009). Limitations of integrated assessment models of climate change. Climatic change , 95 , 297–315. Anderson, K., & Peters, G. (2016). The trouble with negative emissions. Science, 354(6309), 182–183. https://doi.org/10.1126/science . Bauer, N., Baumstark, L., Haller, M., Leimbach, M., Luderer, G., Lueken, M., … Klein, D. (2011). REMIND: The equations. Potsdam Institute of Climate Impact Research Publication . Dooley, K., & Kartha, S. (2018). Land-based negative emissions: Risks for climate mitigation and impacts on sustainable development. International Environmental Agreements: Politics, Law and Economics, 18(1), 79–98. https://doi.org/10.1007/s10784-017-9382-9 Fujimori, S., Hasegawa, T., Krey, V., Riahi, K., Bertram, C., Bodirsky, B. L., … van Vuuren, D. (2019). A multi-model assessment of food security implications of climate change mitigation. Nature Sustainability , 2 (5), 386–396. Fujimori, S., Wu, W., Doelman, J., Frank, S., Hristov, J., Kyle, P., … Takahashi, K. (2022). Land-based climate change mitigation measures can affect agricultural markets and food security. Nature Food , 3 (2), 110–121. https://doi.org/10.1038/s43016-022-00464-4 Holz, Ceecee, Eric Kemp-Benedict, Tom Athanasiou, and Sivan Kartha. "The climate equity reference calculator." Journal of Open Source Software 4, no. 35 (2019): 1273. Huppmann, D., Gidden, M., Fricko, O., Kolp, P., Orthofer, C., Pimmer, M., … Krey, V. (2019). The MESSAGEix Integrated Assessment Model and the ix modeling platform (ixmp): An open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development. Environmental Modelling & Software , 112 , 143–156. IPCC, 2014. "Climate Change 2014: Mitigation of Climate Change—Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. " Cambridge University Press. IPCC, 2023. Report of the Workshop on the Use of Scenarios in the Sixth Assessment Report and Subsequent Assessments . United Nations Economic and Social Commission for Asia and the Pacific. IPCC, 2022: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. doi: 10.1017/9781009157926.001 . Jackson, R. B., Ahlström, A., Hugelius, G., Wang, C., Porporato, A., Ramaswami, A.,… Yin, J. (2022). Human well-being and per capita energy use.Ecosphere, 13(4), e3978. https://doi.org/10.1002/ecs2.3978 Jaiswal, S., Nagarajan, A., & Mythri, A. (2024). Projecting a food insecure world: equity implications of land-based mitigation in IPCC mitigation pathways. Environmental Science & Policy , 155 , 103724. https://doi.org/10.1016/j.envsci.2024.103724 Jayaraman, T., Kanitkar, T., & D'Souza, M. (2012). Equity and burden sharing in emission scenarios: A carbon budget approach. In Handbook of climate change and India (pp. 130–146). Routledge. Kanitkar, T. (2021). Equity in global climate policy and implications for India’s energy future. Economic and Political Weekly , 56 (52), 68–75. Kanitkar, T., Mythri, A., & Jayaraman, T. (2024). Equity assessment of global mitigation pathways in the IPCC Sixth Assessment Report. Climate Policy , 24 (8), 1129–1148. https://doi.org/10.1080/14693062.2024.2319029 Kanitkar, T., & Jayaraman, T. (2025) Climate Justice and Equitable Futures. Retrieved from https://www.boell.de/sites/default/files/2025-02/e-paper_climate_justice_and_equitable_futures.pdf Khan, I., Hou, F., Zakari, A., & Tawiah, V. K. (2021). The dynamic links among energy transitions, energy consumption, and sustainable economic growth: A novel framework for IEA countries. Energy , 222 , 119935. Lamb, W. F., Wiedmann, T., Pongratz, J., Andrew, R., Crippa, M., Olivier, J. G., …Minx, J. (2021). A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018. Environmental research letters , 16 (7), 073005. Lenzen, M., Keyβer, L., & Hickel, J. (2022). Degrowth scenarios for emissions neutrality. Nature Food , 3 (5), 308–309. Li, M., Keyßer, L., Kikstra, J. S., Hickel, J., Brockway, P. E., Dai, N., … Lenzen,M. (2024). Integrated assessment modelling of degrowth scenarios for Australia. Economic Systems Research , 36 (4), 545–575. Muttitt, G., Price, J., Pye, S., & Welsby, D. (2023). Socio-political feasibility of coal power phase-out and its role in mitigation pathways. Nature Climate Change , 13 (2), 140–147. Nordhaus, W. D. 1992. "An Optimal Transition Path for Controlling Greenhouse Gases." Science , 258(5086), 1315–1319. O’Neill, B. C., Kriegler, E., Riahi, K., Ebi, K. L., Hallegatte, S., Carter, T. R.,… Van Vuuren, D. P. (2014). A new scenario framework for climate change research:the concept of shared socioeconomic pathways. Climatic change , 122 , 387–400. Pachauri, S., Pelz, S., Bertram, C., Kreibiehl, S., Rao, N. D., Sokona, Y., & Riahi, K. (2022). Fairness considerations in global mitigation investments. Science , 378 (6624), 1057–1059. Parson, E. A., & Fisher-Vanden, A. K. (1997). Integrated assessment models of global climate change. Annual Review of Energy and the Environment , 22 (1), 589–628. Ranjan, A., Kanitkar, T., & Jayaraman, T. (2024). A new scenario framework for equitable and climate-compatible futures. Climate and Development , 1–13. https://doi.org/10.1080/17565529.2024.2365939 Ranjan, A., & Kanitkar, T. (2025). Energy requirements for sustainable human development. Energy for Sustainable Development , 85 , 101648. Rao, N.D., Min, J. & Mastrucci, A. Energy requirements for decent living in India, Brazil and South Africa. Nat Energy 4, 1025–1032 (2019). https://doi.org/10.1038/s41560-019-0497-9 Riahi, K., Van Vuuren, D. P., Kriegler, E., Edmonds, J., O’neill, B. C., Fujimori,S., … Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global environmental change , 42 , 153–168. Rosen, R. A. (2021). Why the shared socioeconomic pathway framework has not been useful for improving climate change mitigation policy analysis. Technological Forecasting and Social Change , 166 , 120611. Semieniuk, G., L. Taylor, A. Rezai, & Foley, D.K. (2021). Plausible energy demand patterns in a growing global economy with climate policy. Nature Climate Change. 11(4), 313–318. https://doi.org/10.1038/s41558-020-00975-7 Skea, J., Shukla, P., Al Khourdajie, A., & McCollum, D. (2021). Intergovernmental Panel on Climate Change: Transparency and integrated assessment modeling. Wiley Interdisciplinary Reviews: Climate Change , 12 (5), e727. Srikanth, R., & Bhatt, J. R. (2023). Why India needs coal to achieve its sustainable development goals (No. f6dhe_v1). Center for Open Science. Stanton, E. A., Ackerman, F., & Kartha, S. (2009). Inside the integrated assessment models: Four issues in climate economics. Climate and Development , 1 (2), 166–184. Stanton, E. A. (2011). Negishi welfare weights in integrated assessment models: the mathematics of global inequality. Climatic Change , 107 (3), 417–432. United Nations Framework Convention on Climate Change. (1992). S. Treaty Doc No. 102 – 38, 1771 U.N.T.S. 107. Retrieved from https://unfccc.int/resource/docs/convkp/conveng.pdf United Nations Framework Convention on Climate Change. (2022). Decision 1/CP.27: Sharm el-Sheikh Implementation Plan. Retrieved from https://unfccc.int/documents/624444 United Nations Framework Convention on Climate Change. (2023). Decision 1/CMA.5: Outcome of the first global stocktake. Retrieved from https://unfccc.int/documents/643004 Van Vuuren, D. P., Riahi, K., Calvin, K., Dellink, R., Emmerling, J., Fujimori, S.,… O’Neill, B. (2017). The Shared Socio-economic Pathways: Trajectories for human development and global environmental change. Global Environmental Change , 42 , 148–152. van de Ven, D. J., Mittal, S., Nikas, A., Xexakis, G., Gambhir, A., Hermwille, L.,… Peters, G. P. (2025). Energy and socioeconomic system transformation through a decade of IPCC-assessed scenarios. Nature Climate Change , 1–9. van Soest, Heleen L., Detlef P. van Vuuren, Jérôme Hilaire, Jan C. Minx, Mathijs JHM Harmsen, Volker Krey, Alexander Popp, Keywan Riahi, and Gunnar Luderer. "Analysing interactions among sustainable development goals with integrated assessment models." Global Transitions 1 (2019): 210–225. Victor, P. A. (2012). Growth, degrowth and climate change: A scenario analysis. Ecological economics , 84 , 206–212. Vogel, J., Steinberger, J. K., O'Neill, D. W., Lamb, W. F., & Krishnakumar, J. (2021). Socio-economic conditions for satisfying human needs at low energy use: An international analysis of social provisioning. Global Environmental Change , 69 , 102287. https://doi.org/10.1016/j.gloenvcha.2021.102287 Wood, T. W., Richter, K., & Atkins, E. (2024). Modelling beyond growth perspectives for sustainable climate futures: the case for rethinking Shared Socioeconomic Pathways. Energy Research & Social Science , 117 , 103705. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Oct, 2025 Reviews received at journal 14 Oct, 2025 Reviewers agreed at journal 26 Sep, 2025 Reviews received at journal 18 Aug, 2025 Reviewers agreed at journal 31 Jul, 2025 Reviewers invited by journal 28 Jul, 2025 Editor assigned by journal 25 Jul, 2025 Submission checks completed at journal 23 Jul, 2025 First submitted to journal 15 Jul, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7132898","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":491797826,"identity":"6b6844a3-5562-415e-9a22-0574b58901cd","order_by":0,"name":"Tejal Kanitkar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIie3RIQvCQBTA8TeEWxmzHijuKywaRL/KG4NZxGIxyLiVGa02v8qTwSyH1sHKliyGRZO4gWLztiZ4v3bw/rwHB6BpvwmhoAmw95N9m21YTYIUdEvqiJIOV81MUVYoL0vbPBW99ToEeyAUWyzyOWb5ilkL15AyATYkRcIROVa5F8MCjCgmYBwViVP4d6zOXty/Fkb0CFskHIL6MPJijq4RiV6LRGIwRunXyc09ijSxlIm5lX5WpVPvsJuXpdiEI2evSOo9nwmC10cpmNRiSNM07a89AXEVQr1oIQJeAAAAAElFTkSuQmCC","orcid":"","institution":"National Institute of Advanced Studies","correspondingAuthor":true,"prefix":"","firstName":"Tejal","middleName":"","lastName":"Kanitkar","suffix":""},{"id":491797827,"identity":"4ff3808c-fe6f-486b-bf86-e3db4c19e0f7","order_by":1,"name":"T. Jayaraman","email":"","orcid":"","institution":"M.S. Swaminathan Research Foundation","correspondingAuthor":false,"prefix":"","firstName":"T.","middleName":"","lastName":"Jayaraman","suffix":""},{"id":491797828,"identity":"b0b142ba-44dd-4b91-a4a7-dae5710f8888","order_by":2,"name":"V. P. Lavanyaa","email":"","orcid":"","institution":"National Institute of Advanced Studies","correspondingAuthor":false,"prefix":"","firstName":"V.","middleName":"P.","lastName":"Lavanyaa","suffix":""}],"badges":[],"createdAt":"2025-07-15 17:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7132898/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7132898/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87901397,"identity":"ca12f374-715e-4533-83b3-5005ef9c73cd","added_by":"auto","created_at":"2025-07-30 08:18:28","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":196074,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic Overview of Methodology used for the Model for Energy Equity and Climate Compatibility_Version.1 (MEECC_V.1)\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7132898/v1/4a96a31b4de1fefefe27cf67.jpeg"},{"id":87901398,"identity":"aa7171f2-a68d-4afd-a32f-58dd9cb81bd4","added_by":"auto","created_at":"2025-07-30 08:18:28","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132371,"visible":true,"origin":"","legend":"\u003cp\u003eCountry Classification based on 6 different approaches operationalised in the MEECC_V.1.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7132898/v1/29e64d5d1da1a751413d47cb.jpeg"},{"id":87902968,"identity":"ecf275ad-a633-4156-a336-33ac43e71546","added_by":"auto","created_at":"2025-07-30 08:26:28","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":77693,"visible":true,"origin":"","legend":"\u003cp\u003eEmissions projections for Annex-I and Non-Annex-I Parties in scenarios SEA-1 and SEA-2 for baseline (without mitigation) and under carbon budget constraints for limiting warming to 1.5ºC (50% probability), 1.7ºC (50% probability), and 2ºC (67% probability).\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7132898/v1/6fd702b85a37800a27aaed25.jpeg"},{"id":87901412,"identity":"db321958-9cb6-4f75-8f11-741f732c6088","added_by":"auto","created_at":"2025-07-30 08:18:30","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94317,"visible":true,"origin":"","legend":"\u003cp\u003eRequired change in the emissions intensity of energy in the SEA-1 alternative (4a) and SEA-2 alternative (4b) under three temperature targets. Each panel (4a and 4b) consists of scenario outcomes for SEA-1 and SEA-2 under three different temperature targets – 50% probability of limiting warming to 1.5ºC, 50% probability of limiting warming to 1.7ºC, and 67% probability of limiting warming to 2ºC.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7132898/v1/6cfff63aaf789afd64e84280.jpeg"},{"id":87901400,"identity":"db17d1e1-be34-4630-81b3-3a86bd723771","added_by":"auto","created_at":"2025-07-30 08:18:28","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":42218,"visible":true,"origin":"","legend":"\u003cp\u003eRequired change in the emissions intensity of energy in Annex-I and non-Annex-I countries in the SEA-1 and SEA-3 alternatives for a 50% probability of limiting warming to 1.7ºC.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7132898/v1/6d2cdbb3cc0933821732747c.jpeg"},{"id":87901425,"identity":"0060c667-12e5-4b80-a9d0-cc60ab5114f6","added_by":"auto","created_at":"2025-07-30 08:18:30","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":84612,"visible":true,"origin":"","legend":"\u003cp\u003eNet-CO2 emissions in scenarios SEA-1 (6a), SEA-2 (6b), and SEA-3 (6c) under carbon budget constraints for limiting warming to 1.5ºC (50% probability), 1.7ºC (50% probability) and 2ºC (67% probability).\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7132898/v1/6f40186dd7b55a1c8951d8a9.jpeg"},{"id":87901404,"identity":"f843a63c-1ea3-4c60-94ce-a557ceb953a1","added_by":"auto","created_at":"2025-07-30 08:18:29","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":32191,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative emissions between 2020 and 2100 for groups G1 to G5 in scenarios SEA-1 and SEA-3 under carbon budget constraints of limiting warming to 1.7 deg. C (50% probability).\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7132898/v1/aaa837a3255d1bf2a05f7c79.jpeg"},{"id":87901415,"identity":"818e0edc-2624-4b44-96f9-4ad2f6ec6d53","added_by":"auto","created_at":"2025-07-30 08:18:30","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":85450,"visible":true,"origin":"","legend":"\u003cp\u003eGDP [Billion $-Constant 2017-PPP] and per capita GDP [$-Constant 2017-PPP per person] projections in scenarios SEA-1 and SEA-3 for Germany and India (8a) and primary energy [Exa joules-EJ] and per capita primary energy Giga projections in scenarios SEA-1 and SEA-3 for Germany and India (8b).\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7132898/v1/4a65ea43a3c25b32a6c8f64c.jpeg"},{"id":87901417,"identity":"1893fc50-d5a7-4a90-a2b7-1b1a25a60ba6","added_by":"auto","created_at":"2025-07-30 08:18:30","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":141595,"visible":true,"origin":"","legend":"\u003cp\u003eEmissions projections for India under the three alternative (SEA-1, SEA-2, and SEA-3) for two different temperature targets (1.7ºC_50%, and 2ºC_67%) and a range of peaking years (2025 to 2040)\u003c/p\u003e","description":"","filename":"image9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7132898/v1/ebdf6b9d9a8c7d18e94775f4.jpeg"},{"id":87901420,"identity":"c8561d13-ebe9-4eb7-af01-235b76808391","added_by":"auto","created_at":"2025-07-30 08:18:30","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":94305,"visible":true,"origin":"","legend":"\u003cp\u003eEmissions projections for Germany under the three alternatives (SEA-1, SEA-2, and SEA-3) for two different temperature targets (1.7ºC_50%, and 2ºC_67%).\u003c/p\u003e","description":"","filename":"image10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7132898/v1/7ec3401a4cf7ecab63efa0f2.jpeg"},{"id":87903458,"identity":"9f7ae440-75df-4645-b27e-87ce9f0c681d","added_by":"auto","created_at":"2025-07-30 08:34:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2050969,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7132898/v1/a80684c1-0fb5-492a-b9cc-d69a27bcda17.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Projected Global and National Energy and Climate Futures using an alternative Integrated Assessment Framework","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSince the 5th Assessment Report of the IPCC, the process of building climate scenarios has become intrinsically tied to the process of building socio-economic scenarios (van de Ven et al, 2025). The emissions trajectories used by WG-I to estimate associated warming levels and earth system impacts are produced using Integrated Assessment Models (IAMs). These models are meant to provide integrated simulations of the physical, economic, and energy systems and typically do so through the integration of economic models, energy optimisation models, and vegetation models (Parson and Fisher-Vanden, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Skea et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe emissions trajectories produced by the IAMs represent outcomes that depend strongly on a of a range of underlying assumptions (Ackerman et al, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The regional distribution of the mitigation burden, the potential changes in the pace of economic growth and development across regions, the achievement of sustainable development goals, ensuring food security and well-being, among others, represent the range of possibilities for the future that are put together in some coherent mathematical form to arrive at consequent energy and emissions projections. It is possible to arrive at the same emissions trajectory using different combinations of the underlying assumptions (IPCC, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For example, the same emissions trajectory can be achieved through assumptions of continuing global inequality as well as through the elimination of global inequality, or through different assumptions about how the global burden of mitigation is distributed across regions (Ranjan et al, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kanitkar and Jayaraman, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The projections of socio-economic futures that underlie the emissions trajectories are therefore not sacrosanct and robust science requires the exploration of a broad range of possibilities that may result in similar emissions outcomes.\u003c/p\u003e\u003cp\u003eThe scenarios assessed in the 6th Assessment Report (AR6) of the IPCC were produced using IAMs. Five socio-economic baselines called Shared Socio-Economic Pathways or SSPs were constructed and used extensively in the AR6 to explore mitigation policies. The stated purpose of these SSPs was to combine \u0026ldquo;\u003cem\u003epathways of future radiative forcing and their associated climate changes with alternative pathways of socioeconomic development in order to carry out research on climate change impacts, adaptation, and mitigation\u003c/em\u003e\u0026rdquo;. These pathways were meant to represent \u0026ldquo;plausible\u0026rdquo; socio-economic trends for economic, social, and ecological developments over this century, in the absence of climate policies (O\u0026rsquo;Neill et al, 2014; Riahi et al, 2017; Van Vuuren et al, 2017). The proponents of the SSPs argue that this approach allows for the exploration of scenarios that do not just cover a wide range of emissions outcomes, but also a wide range of socio-economic trends and policy outcomes (ibid).\u003c/p\u003e\u003cp\u003eHowever, the literature produced since AR6 shows that the SSPs and the modelling approach used to quantify them, have narrowed rather than broadened the exploration of possible futures (Rosen, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kanitkar et al, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wood et al, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The SSPs as well as the scenarios literature in the climate change domain focus on meeting the temperature targets of the Paris Agreement and thereby mitigation is foregrounded as a primary policy concern across all scenarios. However, developing countries have always maintained, and the framing of the UNFCCC also makes it clear, that for countries that have contributed little to historical greenhouse gas emissions and have significant development deficits, poverty eradication and sustainable development continue to be over-riding priorities (UNFCCC, 1992). Further, the SSP based scenarios have narrowed the possible futures they explore to those that perpetuate a whole range of global inequalities across several key variables, including continuing income disparities, significantly lower levels of energy access in the global South, and the perpetuation of food insecurity and increasing risk of hunger (Kanitkar et al \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jaiswal et al \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). There is no storyline that projects the achievement of even the basic Sustainable Development Goals (SDGs) of zero hunger and poverty eradication (van Soest et al, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jaiswal et al, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The narrow range of socio-economic outcomes as well as well as the unequal distribution of the mitigation burden between developed and developing countries in the scenarios of AR6, is a result of both the assumptions embedded in the SSPs and the approach to modelling that the IAMs use (Kanitkar 2024; Li et al, 2024; Stanton et al, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIt is therefore necessary to rethink and develop new approaches to scenario building that are not constrained by the economic approaches and methodological choices inherently embedded in the current IAMs. In this context we present a new approach to modelling and scenario building in this paper, developed further from the proposed new framework in Ranjan et al (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We demonstrate the use of a user-interface-enabled analytical model, Model for Energy Equity and Climate Compatibility_Version.1 (MEECC_V.1), to facilitate the construction of a range of scenarios to operationalize climate-compatibility, equity, and feasibility. This model is published under the Creative Commons Licence; the Python Code is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.15926615\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.15926615\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and the interactive excel-based model is available on request.\u003c/p\u003e\u003cp\u003eIn contrast to the IAM models predominantly in vogue today, this model framework is developed at a high-level of generality, involving only three essential variables to track the energy-economy-emissions linkages, without elaborating any of these three components in any level of detail at this stage. Such elaboration will be undertaken later. The three variables that we focus on are energy consumption, energy efficiency and the emissions intensity of energy, all of which are taken at the economy-wide level and not disaggregated by sectors. Low-carbon development is considered through trajectories with progressive improvement in energy efficiency of the economy (i.e., decoupling energy and economic growth) and increase in energy use in developing economies to meet at least minimum developmental thresholds, while climate constraints are implemented through cumulative emissions limits. The model enables the comparison of futures with and without convergence and reduction of inter-country inequalities in these key variables, since it is at this level that the gaps in the current IAMs are most starkly visible, despite their considerable complexity and granularity.\u003c/p\u003e\u003cp\u003eWe broadly follow the methodological approach proposed in Ranjan et al (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with some key differences. These differences are a result of making the model more user interactive as well as the introduction of elements that allow for a wider scope in scenario generation. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is a graphical summary of the methodology for the Model for Energy Equity and Climate Compatibility_Version.1, or MEECC_V.1 for short.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe user interface (UI) provides the possibility of building a wide range of socio-economic and energy scenarios for the future. Model results can be used for assessing global outcomes and their regional distribution, as well as for assessing outcomes for individual countries given a set of requirements and assumptions.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the classification of countries into five groups based on the use of all six methods of classification currently in the model. Countries can belong to different groups based on the approach used and also based on the weighting schema selected by the user within a particular approach. This allows for a wider scope for comparison across scenarios. In the illustrative scenarios shown in this paper, we use the UMAP-k-means clustering approach to classify countries in five groups. The methods section of this paper discusses the methods used for country classification in detail.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Scenario Development\u003c/h2\u003e\u003cp\u003eWe demonstrate the model here through the development of three broad alternatives in terms of equity that explore two basic goals \u0026ndash; energy equity and climate equity. Socioeconomic Alternative-1 (SEA-1) is the alternative that explores meeting both energy and climate equity. In SEA1, energy equity is operationalised through the convergence of per capita primary energy to 75 GJ/person/year by 2050. In this scenario, GDP growth is assumed to occur across all regions. Capital scarce countries at lower levels of GDP in 2019 are assumed to grow at faster rates. However, despite this, some least developed countries, especially in groups 4 and 5 do not reach the threshold level of \u003cspan\u003e$\u003c/span\u003e28,000 per person per year that is necessary for the achievement of developmental goals. Therefore, it is assumed that higher GDP growth will be made possible in these countries, enabling the achievement of at least this threshold by 2050. The routes through which this can be made possible can be explored exogenously.\u003c/p\u003e\u003cp\u003eClimate equity is operationalised in this scenario through two assumptions. First, the CO\u003csub\u003e2\u003c/sub\u003e-FFI emissions peak at different times for each country depending on its historical responsibility, i.e. the distance between the fair share and actual emissions of that country between 1850 and 2019. Emissions of countries that have emitted more than their fair share are assumed to have already peaked, or to peak in 2020 itself. Peaking years for other countries are either 2025, 2030, or 2035 based on the degree of difference between their fair and actual shares. Second, each country can access its per capita fair share of the RCB. Taking historical responsibility fully into account would require the consideration of the total global carbon budget and not just the remaining carbon budget (Jayaraman et al, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, this would lead to significant negative emissions for most developed countries in group G1 and G2. Given that the uncertainty of achieving the scale of negative emissions needed and the potential impacts of the same, we do not consider the total carbon budget. However, we use variations in per capita fair share estimate to account for historical responsibility and respective capability as discussed in the previous section. In SEA-1, we consider the per capita fair share weighted by historical cumulative emissions (50% weight) and capability represented by per capita GDP (50% weight).\u003c/p\u003e\u003cp\u003eAlternative SEA-2 projects a world with energy convergence, but without climate equity. Energy and GDP assumptions remain the same as SEA-1 in this storyline, however, the RCB is not allocated according to per capita fair shares. It is assumed that current fast/high emitters will continue to emit and appropriate a higher share of the remaining carbon budget. In this scenario, it is projected that countries share of the RCB will be equivalent to their current (2019) share of annual emissions.\u003c/p\u003e\u003cp\u003eIn alternative SEA-3 (Energy gap and climate inequity) a wide gap remains in primary energy consumption across different development groups even by 2050. By 2050, countries in group G1 either reduce or increase to, and stabilize at 160 GJ/person/year, countries in G2 at 120 GJ, G3 at 75 GJ, G4 at 40 GJ, and in G5 at 25 GJ per person per year. Climate inequity is represented in the same way as in SEA-2. CO2-LULUCF emissions are not varied across these scenarios and are assumed to follow a median path where sinks remain at the levels they are in 2019, and sources reduce to zero by 2050 and remain at zero thereafter. Through these three alternatives, we demonstrate the range of issues that can be explored by using simple analytical models such as the MEECC_V.1. The two key gaps in the scenarios assessed in AR6 were i) the unequal distribution of the mitigation burden between developed and developing countries, violating the principles of equity and CBDR\u0026amp;RC enshrined in the UNFCCC an its Paris Agreement, and ii) the continuation of inequalities in all key developmental variables considered in the models, specifically income and energy consumption. We address both in this paper through the development of the three alternatives described above.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Global Emissions Outcomes\u003c/h2\u003e\u003cp\u003eGlobal outcomes follow expected trends, with sharp reductions required in CO2 emissions to limit warming to well within 2\u0026ordm;C. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the emissions reductions required by 2030, 2035, 2040, and 2050 with respect to emissions in 2020 in each scenario across three temperature targets \u0026ndash; a 50% probability of limiting warming to 1.5\u0026ordm;C, a 50% probability of limiting warming to 1.7\u0026ordm;C, and a 67% probability of limiting warming to 2\u0026ordm;C.\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\u003eGlobal Net-CO2 emissions reductions (below 2020 levels) in scenarios SEA-1, SEA-2, and SEA-3 for three temperature targets \u0026ndash; 50% probability of limiting warming to 1.5\u0026ordm;C, 50% probability of limiting warming to 1.7\u0026ordm;C, and 67% probability of limiting warming to 2\u0026ordm;C.\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eCO2 Emissions \u0026ndash; SEA-1\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2020\u0026ndash;2030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2020\u0026ndash;2035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2020\u0026ndash;2040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2020\u0026ndash;2050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.5\u0026ordm;C (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-73%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-87%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-96%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.7\u0026ordm;C (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-37%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-82%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ordm;C (67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-23%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-35%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-49%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-71%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCO2 Emissions \u0026ndash; SEA-2\u003c/b\u003e\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\u003e2020\u0026ndash;2030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2020\u0026ndash;2035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2020\u0026ndash;2040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2020\u0026ndash;2050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.5\u0026ordm;C (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-49%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-78%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-96%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.7\u0026ordm;C (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-22%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-38%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-53%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-80%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ordm;C (67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-10%\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\u003e-41%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-60%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCO2 Emissions \u0026ndash; SEA-3\u003c/b\u003e\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\u003e2020\u0026ndash;2030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2020\u0026ndash;2035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2020\u0026ndash;2040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2020\u0026ndash;2050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.5\u0026ordm;C (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-98%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.7\u0026ordm;C (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-23%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-39%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-54%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-81%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ordm;C (67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-12%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-21%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-38%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-61%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAcross the three scenarios global emissions reductions do not differ too widely for a particular temperature target. To limit warming to 1.5 deg. C, model outcomes indicate a 100% reduction, i.e. reaching net-zero CO2 emissions globally by 2050 irrespective of socio-economic assumptions in each alternative. However, the regional projections that result in these global outcomes vary widely across the three alternatives SEA-1, SEA-2, and SEA-3.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Results for UNFCCC Groups\u003c/h2\u003e\u003cp\u003eA comparison between the SEA-1 and SEA-2 alternatives allows us to assess the inter-country distribution of the mitigation burden for different temperature targets with and without climate equity. Since the principle of equitable burden sharing based on common but differentiated responsibilities and capabilities is enshrined in the UNFCCC, we can assess the implications of equitable vs. inequitable allocation of the mitigation burden for the UNFCCC groups, i.e. Annex-I parties to the UNFCCC and non-Annex-I parties to the UNFCCC (UNFCCC, 1992). The projections for primary energy consumption and GDP growth remain the same in both the SEA-1 and SEA-2 alternatives. The global future projected is one of converging energy consumption, with a reduction in energy consumption for those above a specified level and increase for those below this level. GDP growth rates for all groups largely follow historical trends, except in cases where countries do not reach a minimum level of \u003cspan\u003e$\u003c/span\u003e28,000 per person per year, by 2050. In such cases, a higher growth rate is assumed in these countries to achieve the minimum threshold for per capita GDP. Baseline emissions (without any additional mitigation) for Annex-I countries show a declining trend because of reducing energy consumption from 187 GJ/person/year in 2019 to 75 GJ/person/year in 2050 (See Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). For non-Annex-I countries, emissions increase in the baseline as energy consumption increases from 53 GJ/person/year in 2019 to 75 GJ/person/year in 2050 (See Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn SEA-1 scenario, under all temperature targets, the required reduction in the emissions intensity of energy is higher for Annex-I parties as compared to non-Annex-I parties (See Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). On the other hand, the required reduction in the emissions intensity of energy is higher for non-Annex-I parties in scenario SEA-2, when climate equity is compromised, under all temperature targets (See Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eIn the SEA-3 alternative, energy consumption for Annex-I countries reduces only marginally from 187 GJ/person/year in 2019 to 148 GJ/person/year in 2050. The increase for non-Annex-I countries is also marginal, from 53 GJ/person/year in 2019 to 56 GJ/person/year in 2050. Additionally, there is no climate equity implemented in this scenario. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the near-term (2020\u0026ndash;2030) reductions in emission intensity required in scenarios SEA-1 and SEA-3 for Annex-I and non-Annex-I countries for a target of limiting warming to 1.7\u0026ordm;C (50% probability). In SEA-1, the Annex-I group is required to reduce its emissions intensity by 6% per year between 2020 and 2030, whereas the non-Annex-I group is required to reduce it by 3% in the same period. The requirement for the Annex-I group reduces significantly to 2% per year in the SEA-3 scenario despite higher energy consumption in this scenario for this group. On the other hand, for non-Annex-I countries the requirement still remains at about 3% per year as most of the mitigation in this group is in fact achieved through a suppression of energy demand itself. The SEA-3 therefore mimics the IPCC AR6 scenarios closely.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Results for Developmental Groups\u003c/h2\u003e\u003cp\u003eIt is also possible to assess results by developmental groups. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(a) shows the emissions projections for the five development groups (with G1 being the most developed and G5 being the least developed group) under carbon budget constraints for three temperature targets for scenario SEA-1. For the same temperature targets, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(b) shows the emissions projections for all five groups for scenario SEA-2 (energy equity but no climate equity), and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(c) shows the projections for SEA-3 (neither energy nor climate equity).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn SEA-2, i.e., when higher energy consumption is ensured but not an equitable share of the carbon budget, the model returns \u0026ldquo;no solution\u0026rdquo; for 53 countries, mostly in groups G4, and G5, for a temperature target of 1.5\u0026ordm;C. This is because a minimum gap of 10 years is required between the year of peak emissions the year of net-zero emissions. Given that countries in groups G4 and G5 are typically the ones that have not over-used their fair per capita share of the carbon budget in the past, the model allows for later peak years for these countries, consequently rendering it impossible to reach net-zero quickly thereafter (within 10 years) given the unequal carbon budget allocations in SEA-2. This does not change even if the constraint on LULUCF emissions (i.e., the degree of CO2 removal that is assumed to be possible), is changed to the maximum possible limit currently allowed in the model. Only if the peaking year for countries in these groups is advanced considerably to 2025, the model returns a mathematically feasible solution. Since SEA-2 is the scenario in which countries are expected to reach energy consumption levels of 75 GJ per person/year by 2050, the model outcomes suggest that achieving these levels of primary energy consumption within a very limited carbon space that is allocated to G4 and G5 countries in SEA-2 is not possible unless their emissions are forced to peak immediately. On the other hand, in SEA-1 where these countries get their fair share of the remaining carbon budget, while burdens are still high for the more stringent temperature target of 1.5\u0026ordm;C (50% probability), the model still returns at least a mathematically feasible solution.\u003c/p\u003e\u003cp\u003eIf neither energy nor climate equity is met, i.e. in SEA-3, constraints of limiting temperature rise to 1.7\u0026ordm;C (50% probability), returns a mathematically feasible solution for most countries, albeit at the cost of a hugely reduced share of the remaining carbon budget for countries in the G4 and G5 groups, which would imply a severe constraint on their development.. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the comparison of the cumulative emissions in scenarios SEA-1 and SEA-3 for a temperature target of 1.7\u0026ordm;C (50% probability).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn contrast, the cumulative emissions of groups G1, G2, and G3 increase in scenario SEA-3 as compared to SEA-1, with the highest expansion for G1, i.e., the most developed countries. Such group-wise analysis allows us to explore the potential trade-offs in meeting multiple goals for countries at various stages of development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Illustrative results for country-level analysis\u003c/h2\u003e\u003cp\u003eIt is also possible to explore the range of possibilities for specific countries. Here we demonstrate the outcomes for two countries \u0026ndash; Germany and India. With the k-means criteria of clustering used so far, Germany is in development group G1 and India is in group G4. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows how these two countries are classified if different classification criteria described in the methods section are used.\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\u003eClassification for Germany and India using six different classification criteria\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\u003eMethod of classification\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup for Germany\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGroup for India\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer capita GDP in 2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer capita CO2 emissions in 2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistorical cumulative emissions as compared to fair share\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombination of per capita GDP and historical cumulative emissions with 50% weights to each\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEqual Weighting of all development variables (excluding emissions)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUMAP\u0026thinsp;+\u0026thinsp;k-means clustering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIrrespective of the classification criteria used, Germany gets classified into group 1. This is true for a large majority of the non-EIT Annex-I countries. On the other hand, based on the classification criteria used, India gets classified in groups G3, G4, or G5, not making it to groups G1 or G2 irrespective of the clustering method used. For the results in the rest of this section, we take the classification from the k-means clustering method, with Germany in G1 and India in G4.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the per capita energy and GDP trends in scenario SEA-1 and SEA-3 for both these countries. The main difference between the two countries is observed in the energy projections for scenarios SEA-1 and SEA-3 where primary energy consumption converges in the former and in the latter the differences remain significant throughout the century.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe implications of equitable and unequitable sharing of the carbon budget for the same energy and economic growth projections under different temperature targets can be estimated in our model. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the range of net-zero years under a range of temperature and peak year constraints for scenarios SEA-1, SEA-2, and SEA-3 for India. For scenario SEA-1, the option for late peaking (peak emissions in 2040) does not yield a mathematically feasible solution for India for the more stringent mitigation target of 1.5 deg. C (50% probability). However, for all other temperature target, irrespective of peaking year, the model provides the results demonstrating the range of possibilities if the carbon budget is allocated equitably. On the other hand, under both the SEA-2 and SEA-3 scenarios, where climate equity is not ensured, meeting the 1.5 deg. C (50% target) is impossible for India even within the loosely defined constraints in the model. Even with significantly constrained primary energy consumption levels in scenario SEA-3 (40 GJ/person) as compared to SEA-2 (75 GJ/person), the mitigation burden imposed on India due to a higher share of the carbon budget being consumed by countries in other groups, makes meeting both the energy and climate targets impossible. Even the 1.7 deg. C (50%) target can only be met with relatively early peaking of emissions and with constraining energy consumption to levels far below those required for sustainable development. This result underscores the significant importance of climate equity.\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\u003eNet-Zero years in Scenarios SEA-1, SEA-2, and SEA-3 for India under a range of temperature and peaking year constraints\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=\"char\" char=\".\" 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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeak Year\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNet-Zero Year \u0026ndash; SEA-1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNet-Zero Year- SEA-2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNet-Zero Year- SEA-3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.5\u0026ordm;C (50%)-Late peaking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.5\u0026ordm;C (50%)-Medium peaking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.5\u0026ordm;C (50%)-Early peaking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.5\u0026ordm;C (50%)-Immediate peaking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.7\u0026ordm;C (50%)-Late peaking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.7\u0026ordm;C (50%)-Medium peaking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.7\u0026ordm;C (50%)-Early peaking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.7\u0026ordm;C (50%)-Immediate peaking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ordm;C (67%)-Late peaking-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ordm;C (67%)-Medium peaking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ordm;C (67%)-Early peaking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2053\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ordm;C (67%)-Immediate peaking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eEmissions in Germany have already peaked. So here, we demonstrate the required net-zero years for Germany under different temperature targets without altering the peaking year, which for the purpose of the model is 2020. As shown 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\u003eNet-Zero years in Scenarios SEA-1, SEA-2, and SEA-3 for Germany under a range of temperature constraints\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=\"char\" char=\".\" 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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNet-Zero Year \u0026ndash; SEA-1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNet-Zero Year \u0026ndash; SEA-2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNet-Zero Year \u0026ndash; SEA-3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.5\u0026ordm;C (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.7\u0026ordm;C (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ordm;C (67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eEven if it consumes a disproportionate share of the remaining carbon budget (i.e. a share proportional to its current annual share of CO2-FFI emissions) as in SEA-2, Germany will have to reach net-zero by 2042, about 8 years before its declared net-zero year, to be aligned with the 1.5 deg. C target (50% probability). This is despite significant reduction in energy consumption in Germany in this scenario, i.e. from 160 GJ to about 75 GJ. Access to only the fair share of its carbon budget for the 1.5 deg. C target (50% probability) would require Germany to advance its net-zero year to 2032.\u003c/p\u003e\u003cp\u003eFigures \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e show the net CO2 emissions for India and Germany for all three scenarios (SEA1, SEA-2, and SEA-3) for temperature targets of 1.7 and 2\u0026ordm;C. The scenarios show the range of projected emissions with and without climate and energy equity. A similar analysis can be undertaken for all countries included in the model.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eAn Integrated Assessment Model is defined by the IPCC as \u0026ldquo;\u003cem\u003ea model that combines representations of environmental processes, economic dynamics, and policy interventions to explore sustainable development pathways and climate mitigation strategies\u003c/em\u003e\u0026rdquo; (IPCC, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Other definitions in the literature are similar (Nordhaus, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; van Vuuren et al., 2011). None of these definitions suggest that the use of economic models with a particular underlying economic logic, or the use of cost-optimisation techniques in energy models is a necessary requirement in the construction of models to qualify under this definition as an IAM. However, this has almost become the norm, with most, if not all, existing IAMs using the same underlying logic and approach. In the new model (MEECC_V.1) presented in this paper, we have proposed an alternative framework for integrated assessment. We integrate economic, energy, and environmental aspects, but in a manner that differs significantly from the current set of IAMs\u003c/p\u003e\u003cp\u003eWe depart from the usual approach used in IAMs in four ways: i) we use a development-based classification of regions and not a geographical classification, ii) we do not use the general equilibrium framework for modelling the socio-economic dimensions, but use overall economy-wide metrics, iii) we do not use carbon prices and global least-cost optimisation as the framework, but use analytical approaches to explore a range of energy futures, iv) we do not assess emissions outcomes as a result of optimising energy consumption and production optimisation, but instead use a range of allocation approaches to distribute the global carbon budget across countries and/or development groups. We do of course have a relatively simple construction of the energy and economy sector, but then we do not miss the wood of energy access and climate equity for the trees of granularity and complexity that are intrinsic to the IPCC AR6 IAMs.\u003c/p\u003e\u003cp\u003eSome of the IAMs also provide results for fossil fuel transitions that are based on optimisation done at the global level. However, each country implements energy policies at the national level. These are not based on some global carbon price considerations, but through planning exercises for the short, medium and long-term considering their own national circumstances and economic and policy constraints. Unlike the downscaling methods for national level considerations in the current IAMs, the results from the MEECC can be used by stakeholders to determine the constraints that can be used to design national energy transition strategies, by using modelling techniques of their choice within the scope of global equity considerations.\u003c/p\u003e\u003cp\u003eOur results demonstrate the potential use of the model in assessing trade-offs between achieving equity in energy futures and equity in sharing of the mitigation burden. In scenarios which meet both energy and climate equity, the burden on Annex-I countries, or countries in the high development groups (G1 and G2) are significantly higher. On the other hand, most countries at low levels of development cannot achieve higher levels of energy consumption to meet developmental needs, in a manner that is also compatible with the global goals of the Paris Agreement, unless they can use their fair share of the carbon budget. The model can also allow users to explore a range of scenarios that meet energy, climate, and developmental targets and the trade-offs between these. This is important in the context of setting targets through the Nationally Determined Contributions (NDCs), or evaluating targets and contributions on the basis of equity. Article 14 of the Paris Agreement requires that \u0026ldquo;\u003cem\u003eParties [to the Agreement] shall periodically take stock of the implementation of this Agreement to assess the collective progress towards achieving the purpose of this Agreement and its long-term goals (referred to as the \u0026ldquo;global stocktake\u0026rdquo;)\u003c/em\u003e\u0026rdquo;. The global stocktake is to be undertaken \u0026ldquo;\u003cem\u003ein the light of equity and the best available science\u003c/em\u003e\u0026rdquo;. Analytical models such as the MEECC can allow for such an analysis to facilitate i) ex ante analysis to enable the setting of fair and scientific targets and ii) ex post assessment of targets on the basis of equity and the best available science. We note that, in our view, there is no current IAM model to provide robust assessments of the equity of various country NDCs in alignment with the principles and values of the UNFCCC and its Paris Agreement.\u003c/p\u003e\u003cp\u003eThe first version of the MEECC that we present here will be available in the public domain and be periodically updated and improved based on feedback from a wide range of stakeholders. We also hope to be able to pursue the coupling of this model with more granular national models to explore the relationship between constraints that operate at the national level and global considerations of alignment with the temperature goals of the Paris Agreement on the basis of equity and justice.\u003c/p\u003e"},{"header":"4. Methods","content":"\u003cp\u003eEach step of model creation is explained in the following sub-sections. We also refer to specific select features of the user interface alongside these explanations.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Country Classification\u003c/h2\u003e\u003cp\u003eUnlike current IAMs, where regions, with the exception of the OECD group of countries, are classified by geography, the MEECC_V.1 uses development-based classification for regions. The model provides users with a choice of parameters based on which regions can be classified. Ranjan et al (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) use principal-component analysis and k-means clustering to classify countries into four development categories. In MEECC_V.1, we classify counties into five groups as this provides a more consistent and uniform distribution of countries across development groups. We use multiple approaches for country classification in the MEECC_V.1 and allow the user to choose a method based on the type of analysis they want to undertake. Currently, six different ways to classify countries into development groups are included in the model, summarized 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\u003eApproaches in the MEECC_V.1 for classifying countries into five developmental groups.\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\u003eBasis for Classification\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. Income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCountries are classified based on per capita GDP for year 2019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. Per capita emissions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCountries are classified based on per capita CO2-FFI emissions in year 2019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3. Historical Responsibility Based\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCountries are classified based on their historical responsibility for past cumulative emissions. This is determined by the difference between their actual emissions and their per capita fair share of the carbon budget between 1850 and 2019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4. CBDR\u0026amp;RC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCountries are classified based on a combination of per capita GDP in 2019 (representing current capability) and the difference between their actual emissions and their fair share of the carbon budget between 1850 and 2019 (representing historical responsibility). A combination of weights can be used to assign relative importance to each of these criteria following Holz et. al (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5. Based on 16 development indicators\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeights can be assigned by users to 16 development variables for income, health, education, infrastructure, energy, and emissions, based on which countries are then classified into development groups.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ek-means clustering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUniform Manifold Approximation and Projection (UMAP), and clustering via the K-Means algorithm. See SI for details.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe development variables used in the last two approaches are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe baseline year for data is 2019 in MEECC_V.1. While historical responsibility for climate change mitigation can be operationalised to some extent by allocating future mitigation burdens based on past emissions and current capacities, its full extent cannot be captured with ever advancing base years in models. This has been true for all models that have been used as inputs to the IPCC assessment process so far, where every generation of models begins from the most recent base year. We hope to address this issue in the next version of the model.\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\u003eVariables for health, education, economy, infrastructure, energy, and emissions used for country classification and model simulations.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eDimension\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eHealth\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eEducation\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eEconomy and Infrastructure\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eEnergy\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eEmissions\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eVariable\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLife Expectancy - LEX\u003c/p\u003e\u003cp\u003e[yrs]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMean Years of Schooling - MYS\u003c/p\u003e\u003cp\u003e[yrs]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDeaths due to unsafe sanitation - DUS\u003c/p\u003e\u003cp\u003e[deaths per 100,000 people]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePer capita primary energy consumption - PPE\u003c/p\u003e\u003cp\u003e[GJ-person\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePer capita CO2-FFI - PCO2-FFI\u003c/p\u003e\u003cp\u003e[tCO2-person\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInfant Mortality - IMR\u003c/p\u003e\u003cp\u003e[deaths per 1000 live births]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaternal Mortality Ration - MMR \u003c/p\u003e\u003cp\u003e[women's deaths per 100,000 live births]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAccess to Internet - IAC\u003c/p\u003e\u003cp\u003e[% of people who used the internet in last 3 months]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eShare of the urban population living in slums - PLS\u003c/p\u003e\u003cp\u003e[% of urban population]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAccess to Electricity - ELEC \u003c/p\u003e\u003cp\u003e[% of households]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDistance from fair share of cumulative CO2-FFI - CCO2-FF1_DFS\u003c/p\u003e\u003cp\u003e[GtCO2]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShare of deaths attributable to air pollution - DAP\u003c/p\u003e\u003cp\u003e[% of total deaths]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDaily Calorific Intake - DAC\u003c/p\u003e\u003cp\u003e[kcal-person\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e-day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDeath rate from unsafe water sources - DUW\u003c/p\u003e\u003cp\u003e[Deaths per 100,000 people]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAccess to Clean Cooking Fuels - CCF\u003c/p\u003e\u003cp\u003e[% of households]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Growth Projections and Macro-Economic Constraints\u003c/h2\u003e\u003cp\u003eThe next step is to specify the projections for economic growth. In most existing IAMs, GDP growth is estimated using macro-economic models, most of which are based on neoclassical economic assumptions. These macro-economic models may be external, to the energy, emissions and land-use calculations, with the GDP projections being exogenous or they may also be internal to the overall model as a component, with the GDP projections being endogenous. For instance, Cobb-Douglas production functions or their variants are often part of these models, and determine total economic output for regions based on assumptions about total factor productivity and factor substitutability for the regions. This applies especially to the IAMs in which GDP is determined endogenously such as the REMIND-MAgPIE model (Bauer et al, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In such models, ostensibly to allow for consistency with \u0026lsquo;past trends\u0026rsquo; in incomes, or to ensure that incomes across regions do not converge at rates that are \u0026lsquo;unsupportable\u0026rsquo; in reality, the distribution of incomes across regions is frozen using Negishi weights (Stanton, 2009). Indeed, modelers themselves have claimed that in the absence of such weighting, the problem of income distribution across regions overwhelms the problem of climate change mitigation (Stanton, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The principle of Pareto-optimality, which is applied as the optimization principle in some models, essentially negates any possibility of distributive justice. This is especially true of distributive climate justice to compensate for historical responsibility for cumulative emissions as well as insufficient emissions reductions that do not adhere to the climate convention\u0026rsquo;s principle that developed countries should take the lead in climate action.\u003c/p\u003e\u003cp\u003eCounter-arguments that welfare and distributive goals can be achieved \u0026ldquo;outside the models\u0026rdquo; (Pachauri et al, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), such as transfer of resources from collections of carbon tax and carbon credit auctions, are problematically circular, as the models themselves are constructed by explicitly ruling out such transfers. Some other models construct regional GDP projections quasi-endogenously, such as in the MESSAGE_GLOBIOM model for example. While the energy sector model in MESSAGE allows for the endogenous re-calculation of GDP based on a range of energy-supply outcomes resulting from least-cost optimisation, this newly calculated GDP is vetted against an exogenously supplied GDP trend and adjusted (\u0026ldquo;calibrated\u0026rdquo; is the term used) if it deviates too much from the latter (Krey et al, 2020). The exogenous GDP trend is in turn produced using macro-economic algorithms that are based on similar neoclassical assumptions discussed above (Huppman et al, 2019). Thus, despite the elaborate computational setup to endogenously calculate GDP in sync with energy use and the emissions from different energy sources in various sectors, the end result is arbitrarily constrained by externally determined GDP growth estimates.\u003c/p\u003e\u003cp\u003eAs a consequence, we do not use such models for GDP projections. In MEECC_V.1 we will simply specify GDP as an empirical indicator of economic activity, focusing on trajectories of GDP growth of countries to focus on inter-country comparisons related to inequalities between countries. Within-country income distribution and equality can be better analysed with more detailed national and sub-national models instead of through global models, or perhaps even be incorporated in this framework, but these considerations are not part of this model. Our strategy does not imply that we argue for GDP itself is a complete measure of well-being. Obviously, outcomes for well-being and development would also depend on the way in which wealth is created and distributed, not all of which is captured by a single measure such as GDP.\u003c/p\u003e\u003cp\u003eFollowing Ranjan et al (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), we construct multiple trajectories for GDP growth. Users can select different GDP trajectories from a range of potential economic growth scenarios constructed for each country group. This list is non-exhaustive and can be further expanded. In the current version of the model, we have three broad growth scenarios and 7 variations across each of these. These are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\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\u003eEconomic Growth Trajectories and Scenario Sets\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=\"left\" 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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScenario Group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScenario Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e1. Uniform growth scenarios\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eGDP growth is seen in for all development groups with rates of growth progressively increasing from G1 to G5. Post 2050, growth rates of G1, G2, G3, G4, G5 are 0%, 0.5%, 1%, 1.5% and 2% for all scenarios.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGDP_UHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh growth for all groups resulting in higher global GDP by 2050.GDP of G1, G2, G3, G4, G5 grows at 2%, 4%, 5%, 6% and 7% respectively.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGDP_UMed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedium growth for all groups resulting in moderately high global GDP by 2050.GDP of G1, G2, G3, G4, G5 grows at 1.5%, 3%, 4%, 5%, and 6% respectively\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGDP_ULow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow growth for all groups resulting in lower global GDP by 2050. GDP of G1, G2, G3, G4, G5 grows at 1%, 2.5%, 3.5%, 4.5% and 5% respectively\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2. Divergent growth scenarios\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eReducing economic growth for groups G1 and/or G2. Post-2050, growth rates of G1, G2, G3, G4, G5 are 0%, 0.5%, 1%, 1.5% and 2% for all scenarios.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGDP_DG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGDP of G1, G2, G3, G4, G5 changes at 0%, 3%, 4%,5% and 6% respectively\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGDP_DG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGDP of G1, G2, G3, G4, G5 grows at -1%, 2%, 4%, 5%, and 6% respectively\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3. Threshold scenarios\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eA minimum threshold of \u003cspan\u003e$\u003c/span\u003e28,000 for per capita GDP is assumed for all countries. Post-2050, growth rates of G1, G2, G3, G4, G5 are 0%, 0.5%, 1%, 1.5% and 2% for all scenarios.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGDP_UMod-TS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGDP growth same as GDP_UMed scenario except for countries which do not reach \u003cspan\u003e$\u003c/span\u003e28,000 per capita GDP till 2050. Higher growth assumed for these countries to achieve threshold\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGDP_DG2-TS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGDP growth same as GDP_DG2 scenario except for countries which do not reach \u003cspan\u003e$\u003c/span\u003e28,000 per capita GDP till 2050. Higher growth assumed for these countries to achieve threshold\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe approach used in the MEECC_V.1 allows for a range of possible GDP indexed futures. The degree of decoupling between GDP and energy can then be assessed depending on the potential economic and energy growth that is projected. This also allows us to assess trade-offs between climate action and energy use in a much more transparent manner than can be done using the general equilibrium models.\u003c/p\u003e\u003cp\u003eFull convergence in GDP is only possible by assuming either slower or reducing economic activity levels in richer countries. While it is technically possible to model such scenarios in the MEECC_V.1, we do not emphasize these scenarios in our results. The implications of such shifts in GDP growth, especially for poorer populations even within rich economies, are highly contested. Even where reducing levels of economic activity is possible \u0026ndash; for example in high income economies with capital saturation, ensuring that such policies are based on principles of distributive justice rather than on further exacerbating income inequalities would require significant transformation in political and economic perspectives. Since global models, either the current IAMs or the MEECC_V.1 do not (and to an extent also cannot) do this, we refrain from using such scenarios in our results, even where they have been argued to be applicable, i.e., for rich economies (Victor, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lenzen et al, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al, 2024).\u003c/p\u003e\u003cp\u003eInstead, we project a range of values for future GDP growth with higher rates of economic growth in developing countries, enabling scenarios with faster reduction of the gaps between groups of countries. Additionally, in some scenarios, we set a minimum threshold of per capita GDP which must be achieved at least by 2050 in all countries, to facilitate the achievement of well-being levels across multiple development indicators. Currently we set that threshold at \u003cspan\u003e$\u003c/span\u003e28,000/person/yr following Ranjan et al (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In future versions of the model, this can be developed further.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Energy Consumption\u003c/h2\u003e\u003cp\u003eIn the current set of IAMs, primary energy consumption estimates are closely related to or based on projections for GDP and other economic variables. As the demand sectors are disaggregated differently with varying assumptions of demand-side behaviour across models, estimates of final energy consumption can be different across scenarios. However, the strong dependence on economic variables in current IAMs leads to very similar outcomes for primary energy consumption across scenarios (Kanitkar et al, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast, in the MEECC_V.1, users can choose GDP and energy pathways independently for building any scenario, and the resultant changes in the energy-GDP relationship represent the degree of decoupling that would be required to achieve the desired outcomes. While it is true that there is a strong correlation between GDP and energy consumption historically, this relationship would need to change (IPCC, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in the face of the increasing need to address global warming. However, this change would have an impact on the nature of economic growth in a country given that production processes for conventional and non-conventional energy vary widely and their supply chains are distributed very differently across the world (Khan et al, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the current version of our model, the user can choose 8 potential pathways from two scenario sets. This list can be expanded further in subsequent versions of the model.\u003c/p\u003e\u003cp\u003eThere are a range of potential energy thresholds corresponding to development indicators that have been explored in the literature (Ranjan and Kanitkar \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In our model, we use these estimates from the literature to construct scenarios that either project energy convergence or divergence across the five development groups. This is similar to the methodology followed in Ranjan et al (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the energy convergence scenario-set, users can choose energy conversion thresholds ranging from 60 to 95 GJ. In this scenario-set, countries or development groups above these thresholds are assumed to move towards these thresholds by the target year and those below these are projected to increase and then stabilize at these threshold levels. In the scenario sets with no convergence, the target energy consumption level for each development group is input separately. Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e summarizes the scenario sets that users can choose from.\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\u003eScenario sets for primary energy consumption in MEECC_V.1\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\u003eScenario Group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScenario Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eConvergence scenarios: Energy convergence to a particular level by 2050, eventual convergence to 70 GJ for all by 2100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eECOV_GlAvg75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAll groups converge at 75 GJ/p by 2050 and remain at this level beyond 2050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eECOV_QPT60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAll groups converge at 60 GJ/p by 2050 and remain at this level beyond 2050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eECOV_QPT70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAll groups converge at 70 GJ/p by 2050 and remain at this level beyond 2050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eECOV_QPT80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAll groups converge at 80 GJ/p by 2050 and remain at this level beyond 2050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eECOV_QPT90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAll groups converge at 90 GJ/p by 2050 and remain at this level beyond 2050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eECOV_QPT95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAll groups converge at 95 GJ/p by 2050 and remain at this level beyond 2050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDivergence scenarios: Energy consumption different for different groups, eventual convergence to 70 GJ for all by 2100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEDIV_High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh divergence: G1 reaches or reduces to 200 GJ by 2050 and then remains at this level; G2 reaches or reduces to 160 GJ and remains at this level; G3 reaches or reduces to 55 GJ and then remains at this level; G4 reaches or reduces to 30 GJ and then remains at this level. G5 reaches or reduces to 15 GJ and then remains at this level\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEDIV_Med\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedium divergence: G1 reaches or reduces to 160 GJ by 2050 and then remains at this level; G2 reaches or reduces to 120 GJ and remains at this level; G3 reaches or reduces to 75 GJ and then remains at this level; G4 reaches or reduces to 40 GJ and then remains at this level. G5 reaches or reduces to 25 GJ and then remains at this level\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe user can also select the rate at which the relationship between primary and final energy changes across the modelling time-period. In the current version of the model, this is assumed to be the same for all development groups given that, in the first instance, energy efficiency improvements or shifts in production processes can work in similar fashion wherever they are adopted.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.4 CO\u003csub\u003e2\u003c/sub\u003e emissions\u003c/h2\u003e\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e emissions from fossil fuel combustion and industrial production (CO2-FFI) and CO\u003csub\u003e2\u003c/sub\u003e emissions from land use and land use change and forestry (CO2-LULUCF) are considered separately in the MEECC_V.1. This version of the MEECC does not include non-CO2 GHG emissions. This will be included in later stages as other elements of human and climate systems interactions are included.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-LULUCF\u003c/em\u003e\u003c/p\u003e\u003cp\u003eEstimates of CO\u003csub\u003e2\u003c/sub\u003e-LULUCF emissions have a much higher degree of uncertainty \u0026ndash; both for historical and current emissions (Lamb et al, 2021). Additionally, the degree and rate at which CO\u003csub\u003e2\u003c/sub\u003e-LULUCF emissions can change is also highly uncertain. Large-scale land-based mitigation has been shown to have serious implications for food security (Fujimori et al, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fujimori et al, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jaiswal et al, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, the actual scale of possible carbon dioxide removal (CDR) from other sources is as yet uncertain, and the effectiveness of some of the methods in ensuring long-term and sustained CO\u003csub\u003e2\u003c/sub\u003e removal is also contested (Anderson and Peters, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Dooley and Kartha, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, while the model does include CO\u003csub\u003e2\u003c/sub\u003e-LULUCF emissions, we have, in the current version, three fixed scenarios through which we project changes in these emissions for the future (See Table\u0026nbsp;\u003cspan refid=\"Tab10\" 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\u003eCO2-LULUCF Scenarios in in MEECC_V.1\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\u003eScenario Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLULUCF_BY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnnual CO2-LULUCF emissions remain the same as base year, i.e. sources remain sources and sinks remain sinks\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLULUCF_PartShift\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSinks remain the same as 2019 and sources become zero-emission by 2050 and remain at zero thereafter\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLULUCF_Shift\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll regions with net-positive CO\u003csub\u003e2\u003c/sub\u003e-LULUCF emissions reduce to zero by 2050 and a maximum of -0.005 GtCO2 by 2100. Those below \u0026minus;\u0026thinsp;0.005 GtCO2, in 2019 remain at 2019 levels.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGlobal CO\u003csub\u003e2\u003c/sub\u003e-LULUCF emissions in 2019 are estimated to be about 4.53 GtCO\u003csub\u003e2\u003c/sub\u003e (Ref-PRIMAP database). The land sector is therefore currently a source of GHG emissions at the global level. In the LULUCF_BY scenario, CO\u003csub\u003e2\u003c/sub\u003e-LULUCF emissions are assumed to remain at this level through the century, with a cumulative emission of 82 GtCO2 globally from LULUCF between 2020 and 2100. In the LULUCF_PartShift scenario cumulative CO\u003csub\u003e2\u003c/sub\u003e-LULUCF emissions between 2020 and 2100 are 42 GtCO2, with annual emissions in 2050 reaching \u0026minus;\u0026thinsp;0.42 GtCO\u003csub\u003e2\u003c/sub\u003e and in the LULUCF_Shift scenario cumulative emissions between 2020 and 2100 are 25.53 GtCO\u003csub\u003e2\u003c/sub\u003e with annual emissions in 2100 reaching \u0026minus;\u0026thinsp;1.72 GtCO\u003csub\u003e2\u003c/sub\u003e. The total negative emissions between 2020 and 2100 range between 34\u0026ndash;57 GtCO2 across all scenarios, which is very conservative as compared to carbon-dioxide removal projected by scenarios assessed in AR6 (Kanitkar et al, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jaiswal et al, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cem\u003eCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-FFI\u003c/em\u003e\u003c/p\u003e\u003cp\u003eFor projecting the potential and required behaviour of CO2-FFI emissions, three inputs are required to the model: i) choice of temperature target with a corresponding probability of achieving that target, ii) the principle based on which the remaining carbon budget will be distributed between countries, and iii) the peaking year for countries in each development category.\u003c/p\u003e\u003cp\u003eThe emissions intensity of energy improves with improving technology, increases in scales of production, and shifts in technology use. This may happen also without any specific policies targeted towards climate change mitigation, as fuel use becomes more efficient and due to technological advances that are driven by other factors However, it is difficult to attribute the rate of such improvements in the absence of targeted policies, as in most countries targeted policies for efficiency improvement already exist and have existed for some time. In the current version of the MEECC_V.1 we assume that the level of emissions intensity of energy for each country remains at 2019 levels in the baseline projections. The required rate of change in this indicator is then estimated under different temperature scenarios.\u003c/p\u003e\u003cp\u003eIn the MEECC_V.1, scenarios can be constructed for different temperature targets, which we currently fix to be 1.5\u0026ordm;C, 1.7\u0026ordm;C, and 2\u0026ordm;C, with corresponding probabilities of either 50% or 67% of limiting warming to these levels. The corresponding carbon budgets, i.e. cumulative CO\u003csub\u003e2\u003c/sub\u003e emissions from 2020 till the point of global net-zero emission for these temperature targets are available from IPCC (2021). These carbon budget values assume certain trajectories of non-CO\u003csub\u003e2\u003c/sub\u003e GHG emissions and the quantum of the remaining carbon budget that will actually be available depends on these assumptions. Currently we assume that these assumptions for non-CO\u003csub\u003e2\u003c/sub\u003e GHG emissions will hold as we do not model non-CO\u003csub\u003e2\u003c/sub\u003e GHG emissions in the MEECC_V.1. We recognize that the behaviour of non-CO\u003csub\u003e2\u003c/sub\u003e GHG emissions is also policy dependent and that the level of non-CO\u003csub\u003e2\u003c/sub\u003e GHG emissions will impact the distribution of the CO\u003csub\u003e2\u003c/sub\u003e budget across countries, and we hope to address this in subsequent versions of the model.\u003c/p\u003e\u003cp\u003eThe next step is to choose the principle through which the remaining carbon budget (RCB) corresponding to a temperature target will be distributed between regions. In the MEECC_V.1, this is implemented through five scenarios (See Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The shares of the RCB accruing to each group as a result of each of these allocation rules is shown in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e10\u003c/span\u003e. As the difference between the equity-based allocation rules is small, we show a comparison of the two broad approaches \u0026ndash; considering or ignoring climate equity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of the Remaining Carbon Budget (RCB) between countries\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\u003eScenario Group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScenario Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eEquity Based\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCFS_GF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer capita fair share of the RCB based on 2019 population, not accounting for historical responsibility or respective capability\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCFS_Hist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer capita fair share of the RCB based on 2019 population, weighted by historical responsibility - weighted by emissions between 1850 and 2019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCFS_Cap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer capita fair share of the RCB based on 2019 population, weighted by capability - weighted by per capita GDP in 2019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCFS_HistCap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer capita fair share of the RCB based on 2019 population, weighted by responsibility and capability - weighted by per capita GDP in 2019 and historical emissions between 1850 and 2019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInequitable grandfathering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCUS_CurrAnn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer capita share of the RCB same as annual share of current (2019) emissions\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eShare of the Remaining Carbon Budget corresponding to user choice for allocation rule\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEquity based allocation (PCFS_HistCap)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInequitable grandfathering\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e54.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSince the MEECC_V.1 is not an optimisation model, we need to define one additional parameter to be able to construct an emissions trajectory for the future. This parameter is the year at which net-CO\u003csub\u003e2\u003c/sub\u003e emissions will peak in each country or region. We allow for two possible routes for peak-year selection: i) the peaking year for each group can be manually selected by users for each development group or ii) the peaking year can be estimated based on the difference between actual share of past cumulative emissions and fair share. The first method is useful when one wants to explore the possibilities for a particular country given a range of peaking years. The second method operationalises decisions of the UNFCCC that have been restated in multiple decisions since the Paris Agreement that peaking years for emissions will differ for developed and developing countries taking into consideration the latter\u0026rsquo;s priorities for poverty eradication and sustainable development (UNFCCC, 2023). Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), is used to estimate the year of net-zero emissions for each country.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{NZy}_{i}=\\frac{2\\times\\:{CES}_{i}}{{E}_{i}^{{PY}_{i}}}+BY$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere subscript \u003cem\u003ei\u003c/em\u003e indexes countries, \u003cem\u003eNZy\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the year of net-zero emissions, \u003cem\u003eCES\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, the cumulative emissions between year of peak and net-zero emissions (i.e. the share of the carbon budget from 2020 onwards after subtracting the cumulative emissions between 2020 and the peak year), \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003ePYi\u003c/em\u003e\u003c/sup\u003e, the annual emissions in the peak year, and \u003cem\u003eBY\u003c/em\u003e, the base year which is 2020 in this case. Linear reductions are assumed between the year of peak and net-zero emissions. The emissions pathway is assumed to follow the baseline trajectory till the time of peak emissions, after which emissions reductions begin.\u003c/p\u003e\u003cp\u003eGiven that there is almost no carbon budget left for the more stringent temperature target of 1.5 deg. C, the model forces drastic reductions in emissions beyond the peaking year in this case to maintain cumulative emissions within the allocated share of the carbon budget. Such drastic reductions are highly unlikely to be feasible technologically, economically, and politically. The gap between the year of peak emissions and net-zero emissions can vary widely. Since no major economy has as yet achieved net-zero emissions, the actual number of years between peak and net-zero emissions can only be speculated upon. For the EU27, for example \u0026ndash; CO2 emissions peaked around 1990 and the target year for net-zero emissions has been pledged to be 2050, indicating a gap of 60 years between peak and net-zero emissions allowing for a gradual transition in fossil fuel use, not in keeping with the rhetoric of a climate emergency. For the US, CO2 emissions peaked around 2005. Before the US withdrawal from the Paris Agreement, it had announced its intension to reach net-zero emissions by 2050. This still allowed for a gap of 45 years between the peak and net-zero emissions. China has announced that its emissions will peak before 2030 to reach net-zero by 2060, which is a gap of about 30 years between peak and net-zero emissions. India\u0026rsquo;s emissions have not yet peaked, nor are they likely to peak before 2040 or even later, given the significantly lower levels of current per capita emissions, the continuing rising demand for energy, and the lack of domestically available low-carbon alternative or \u0026lsquo;transition fuels\u0026rsquo; for coal-based energy in the near term (Kanitkar, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Srikanth and Bhatt, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nevertheless, India has announced a net-zero target year of 2070, which means the gaps between peak and net-zero emissions will at most be 35 years, and likely much lesser.\u003c/p\u003e\u003cp\u003eThe feasibility of being able to achieve net zero emissions under constraints of fair carbon shares depends on a range of factors including, inter alia, levels at which emissions peak, availability of technology, financial capacity and flexibility, levels of energy and other developmental deficits, and political will. Most models address this issue through a mathematical fix of imposing some constraints on the rate of feasible emissions reduction, or the degree of emissions uptake by natural or anthropogenically enhanced carbon sinks, and so on. These constraints are subjective in nature, even though they are not explicitly stated in the published literature, except in some post-facto assessments of these model assumptions (Semieniuk et al, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Muttit et al, 2023). Being able to explore the behaviour of emissions under various scenarios can however allow countries to better assess the implications of targets they set for themselves in the context of equitable or inequitable global climate action. Assessments of feasibility can then be carried out at the country level to determine which scenarios are more feasible and/or more equitable than others. In the current version of the MEECC_V.1, the lowest possible gap between peak and net-zero emissions is 10 years and the highest gap is 50 years.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003e5. Data Availability\u003c/h2\u003e\n\u003cp\u003eAll baseline data for decision variables is available at https://doi.org/10.5281/zenodo.15926615 \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e6. Code Availability\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003ePython code and dash board information is available at https://doi.org/10.5281/zenodo.15926615\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e7. Acknowledgements\u003c/h2\u003e\n\u003cp\u003eThis study has not received any external funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8. Author Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first author was responsible for creating the first version of the model, including the conceptualisation, data collection, analysis, and coding, and also writing the first draft of the paper and creating the figures and tables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe second author was responsible for conceptualising the model, data analysis, and writing and editing the paper.\u003c/p\u003e\n\u003cp\u003eThe third author was responsible for modelling the database and user interface in python. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e9. \u0026nbsp;Competing Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAckerman, F., DeCanio, S. J., Howarth, R. B., \u0026amp; Sheeran, K. (2009). Limitations of integrated assessment models of climate change. \u003cem\u003eClimatic change\u003c/em\u003e, \u003cem\u003e95\u003c/em\u003e, 297\u0026ndash;315.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnderson, K., \u0026amp; Peters, G. (2016). The trouble with negative emissions. Science, 354(6309), 182\u0026ndash;183. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science\u003c/span\u003e\u003cspan address=\"10.1126/science\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBauer, N., Baumstark, L., Haller, M., Leimbach, M., Luderer, G., Lueken, M., \u0026hellip; Klein, D. (2011). REMIND: The equations. \u003cem\u003ePotsdam Institute of Climate Impact Research Publication\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDooley, K., \u0026amp; Kartha, S. (2018). Land-based negative emissions: Risks for climate mitigation and impacts on sustainable development. International Environmental Agreements: Politics, Law and Economics, 18(1), 79\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10784-017-9382-9\u003c/span\u003e\u003cspan address=\"10.1007/s10784-017-9382-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFujimori, S., Hasegawa, T., Krey, V., Riahi, K., Bertram, C., Bodirsky, B. L., \u0026hellip; van Vuuren, D. (2019). A multi-model assessment of food security implications of climate change mitigation. \u003cem\u003eNature Sustainability\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(5), 386\u0026ndash;396.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFujimori, S., Wu, W., Doelman, J., Frank, S., Hristov, J., Kyle, P., \u0026hellip; Takahashi, K. (2022). Land-based climate change mitigation measures can affect agricultural markets and food security. \u003cem\u003eNature Food\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(2), 110\u0026ndash;121.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43016-022-00464-4\u003c/span\u003e\u003cspan address=\"10.1038/s43016-022-00464-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHolz, Ceecee, Eric Kemp-Benedict, Tom Athanasiou, and Sivan Kartha. \"The climate equity reference calculator.\" \u003cem\u003eJournal of Open Source Software\u003c/em\u003e 4, no. 35 (2019): 1273.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuppmann, D., Gidden, M., Fricko, O., Kolp, P., Orthofer, C., Pimmer, M., \u0026hellip; Krey, V. (2019). The MESSAGEix Integrated Assessment Model and the ix modeling platform (ixmp): An open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development. \u003cem\u003eEnvironmental Modelling \u0026amp; Software\u003c/em\u003e, \u003cem\u003e112\u003c/em\u003e, 143\u0026ndash;156.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIPCC, 2014. \u003cem\u003e\"Climate Change 2014: Mitigation of Climate Change\u0026mdash;Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.\u003c/em\u003e\" Cambridge University Press.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIPCC, 2023. \u003cem\u003eReport of the Workshop on the Use of Scenarios in the Sixth Assessment Report and Subsequent Assessments\u003c/em\u003e. United Nations Economic and Social Commission for Asia and the Pacific.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIPCC, 2022: Summary for Policymakers. In: Climate Change 2022: \u003cem\u003eMitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\u003c/em\u003e[P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/9781009157926.001\u003c/span\u003e\u003cspan address=\"10.1017/9781009157926.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJackson, R. B., Ahlstr\u0026ouml;m, A., Hugelius, G., Wang, C., Porporato, A., Ramaswami, A.,\u0026hellip; Yin, J. (2022). Human well-being and per capita energy use.Ecosphere, 13(4), e3978. https://doi.org/10.1002/ecs2.3978\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJaiswal, S., Nagarajan, A., \u0026amp; Mythri, A. (2024). Projecting a food insecure world: equity implications of land-based mitigation in IPCC mitigation pathways. \u003cem\u003eEnvironmental Science \u0026amp; Policy\u003c/em\u003e, \u003cem\u003e155\u003c/em\u003e, 103724. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envsci.2024.103724\u003c/span\u003e\u003cspan address=\"10.1016/j.envsci.2024.103724\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJayaraman, T., Kanitkar, T., \u0026amp; D'Souza, M. (2012). Equity and burden sharing in emission scenarios: A carbon budget approach. In \u003cem\u003eHandbook of climate change and India\u003c/em\u003e (pp. 130\u0026ndash;146). Routledge.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanitkar, T. (2021). Equity in global climate policy and implications for India\u0026rsquo;s energy future. \u003cem\u003eEconomic and Political Weekly\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e(52), 68\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanitkar, T., Mythri, A., \u0026amp; Jayaraman, T. (2024). Equity assessment of global mitigation pathways in the IPCC Sixth Assessment Report. \u003cem\u003eClimate Policy\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(8), 1129\u0026ndash;1148. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/14693062.2024.2319029\u003c/span\u003e\u003cspan address=\"10.1080/14693062.2024.2319029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanitkar, T., \u0026amp; Jayaraman, T. (2025) Climate Justice and Equitable Futures. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.boell.de/sites/default/files/2025-02/e-paper_climate_justice_and_equitable_futures.pdf\u003c/span\u003e\u003cspan address=\"https://www.boell.de/sites/default/files/2025-02/e-paper_climate_justice_and_equitable_futures.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhan, I., Hou, F., Zakari, A., \u0026amp; Tawiah, V. K. (2021). The dynamic links among energy transitions, energy consumption, and sustainable economic growth: A novel framework for IEA countries. \u003cem\u003eEnergy\u003c/em\u003e, \u003cem\u003e222\u003c/em\u003e, 119935.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLamb, W. F., Wiedmann, T., Pongratz, J., Andrew, R., Crippa, M., Olivier, J. G., \u0026hellip;Minx, J. (2021). A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018. \u003cem\u003eEnvironmental research letters\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(7), 073005.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLenzen, M., Keyβer, L., \u0026amp; Hickel, J. (2022). Degrowth scenarios for emissions neutrality. \u003cem\u003eNature Food\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(5), 308\u0026ndash;309.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, M., Key\u0026szlig;er, L., Kikstra, J. S., Hickel, J., Brockway, P. E., Dai, N., \u0026hellip; Lenzen,M. (2024). Integrated assessment modelling of degrowth scenarios for Australia. \u003cem\u003eEconomic Systems Research\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(4), 545\u0026ndash;575.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuttitt, G., Price, J., Pye, S., \u0026amp; Welsby, D. (2023). Socio-political feasibility of coal power phase-out and its role in mitigation pathways. \u003cem\u003eNature Climate Change\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(2), 140\u0026ndash;147.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNordhaus, W. D. 1992. \u003cem\u003e\"An Optimal Transition Path for Controlling Greenhouse Gases.\" Science\u003c/em\u003e, 258(5086), 1315\u0026ndash;1319.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026rsquo;Neill, B. C., Kriegler, E., Riahi, K., Ebi, K. L., Hallegatte, S., Carter, T. R.,\u0026hellip; Van Vuuren, D. P. (2014). A new scenario framework for climate change research:the concept of shared socioeconomic pathways. \u003cem\u003eClimatic change\u003c/em\u003e, \u003cem\u003e122\u003c/em\u003e, 387\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePachauri, S., Pelz, S., Bertram, C., Kreibiehl, S., Rao, N. D., Sokona, Y., \u0026amp; Riahi, K. (2022). Fairness considerations in global mitigation investments. \u003cem\u003eScience\u003c/em\u003e, \u003cem\u003e378\u003c/em\u003e(6624), 1057\u0026ndash;1059.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParson, E. A., \u0026amp; Fisher-Vanden, A. K. (1997). Integrated assessment models of global climate change. \u003cem\u003eAnnual Review of Energy and the Environment\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(1), 589\u0026ndash;628.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRanjan, A., Kanitkar, T., \u0026amp; Jayaraman, T. (2024). A new scenario framework for equitable and climate-compatible futures. \u003cem\u003eClimate and Development\u003c/em\u003e, 1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/17565529.2024.2365939\u003c/span\u003e\u003cspan address=\"10.1080/17565529.2024.2365939\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRanjan, A., \u0026amp; Kanitkar, T. (2025). Energy requirements for sustainable human development. \u003cem\u003eEnergy for Sustainable Development\u003c/em\u003e, \u003cem\u003e85\u003c/em\u003e, 101648.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRao, N.D., Min, J. \u0026amp; Mastrucci, A. Energy requirements for decent living in India, Brazil and South Africa. Nat Energy 4, 1025\u0026ndash;1032 (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41560-019-0497-9\u003c/span\u003e\u003cspan address=\"10.1038/s41560-019-0497-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRiahi, K., Van Vuuren, D. P., Kriegler, E., Edmonds, J., O\u0026rsquo;neill, B. C., Fujimori,S., \u0026hellip; Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. \u003cem\u003eGlobal environmental change\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e, 153\u0026ndash;168.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosen, R. A. (2021). Why the shared socioeconomic pathway framework has not been useful for improving climate change mitigation policy analysis. \u003cem\u003eTechnological Forecasting and Social Change\u003c/em\u003e, \u003cem\u003e166\u003c/em\u003e, 120611.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSemieniuk, G., L. Taylor, A. Rezai, \u0026amp; Foley, D.K. (2021). Plausible energy demand patterns in a growing global economy with climate policy. Nature Climate Change. 11(4), 313\u0026ndash;318. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41558-020-00975-7\u003c/span\u003e\u003cspan address=\"10.1038/s41558-020-00975-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSkea, J., Shukla, P., Al Khourdajie, A., \u0026amp; McCollum, D. (2021). Intergovernmental Panel on Climate Change: Transparency and integrated assessment modeling. \u003cem\u003eWiley Interdisciplinary Reviews: Climate Change\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(5), e727.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSrikanth, R., \u0026amp; Bhatt, J. R. (2023). \u003cem\u003eWhy India needs coal to achieve its sustainable development goals\u003c/em\u003e (No. f6dhe_v1). Center for Open Science.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStanton, E. A., Ackerman, F., \u0026amp; Kartha, S. (2009). Inside the integrated assessment models: Four issues in climate economics. \u003cem\u003eClimate and Development\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(2), 166\u0026ndash;184.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStanton, E. A. (2011). Negishi welfare weights in integrated assessment models: the mathematics of global inequality. \u003cem\u003eClimatic Change\u003c/em\u003e, \u003cem\u003e107\u003c/em\u003e(3), 417\u0026ndash;432.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUnited Nations Framework Convention on Climate Change. (1992). S. Treaty Doc No. 102\u0026thinsp;\u0026ndash;\u0026thinsp;38, 1771 U.N.T.S. 107. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://unfccc.int/resource/docs/convkp/conveng.pdf\u003c/span\u003e\u003cspan address=\"https://unfccc.int/resource/docs/convkp/conveng.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUnited Nations Framework Convention on Climate Change. (2022). \u003cem\u003eDecision 1/CP.27: Sharm el-Sheikh Implementation Plan.\u003c/em\u003e Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://unfccc.int/documents/624444\u003c/span\u003e\u003cspan address=\"https://unfccc.int/documents/624444\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUnited Nations Framework Convention on Climate Change. (2023). \u003cem\u003eDecision 1/CMA.5: Outcome of the first global stocktake.\u003c/em\u003e Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://unfccc.int/documents/643004\u003c/span\u003e\u003cspan address=\"https://unfccc.int/documents/643004\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Vuuren, D. P., Riahi, K., Calvin, K., Dellink, R., Emmerling, J., Fujimori, S.,\u0026hellip; O\u0026rsquo;Neill, B. (2017). The Shared Socio-economic Pathways: Trajectories for human development and global environmental change. \u003cem\u003eGlobal Environmental Change\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e, 148\u0026ndash;152.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan de Ven, D. J., Mittal, S., Nikas, A., Xexakis, G., Gambhir, A., Hermwille, L.,\u0026hellip; Peters, G. P. (2025). Energy and socioeconomic system transformation through a decade of IPCC-assessed scenarios. \u003cem\u003eNature Climate Change\u003c/em\u003e, 1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan Soest, Heleen L., Detlef P. van Vuuren, J\u0026eacute;r\u0026ocirc;me Hilaire, Jan C. Minx, Mathijs JHM Harmsen, Volker Krey, Alexander Popp, Keywan Riahi, and Gunnar Luderer. \"Analysing interactions among sustainable development goals with integrated assessment models.\" \u003cem\u003eGlobal Transitions\u003c/em\u003e 1 (2019): 210\u0026ndash;225.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVictor, P. A. (2012). Growth, degrowth and climate change: A scenario analysis. \u003cem\u003eEcological economics\u003c/em\u003e, \u003cem\u003e84\u003c/em\u003e, 206\u0026ndash;212.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVogel, J., Steinberger, J. K., O'Neill, D. W., Lamb, W. F., \u0026amp; Krishnakumar, J. (2021). Socio-economic conditions for satisfying human needs at low energy use: An international analysis of social provisioning. \u003cem\u003eGlobal Environmental Change\u003c/em\u003e, \u003cem\u003e69\u003c/em\u003e, 102287. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gloenvcha.2021.102287\u003c/span\u003e\u003cspan address=\"10.1016/j.gloenvcha.2021.102287\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWood, T. W., Richter, K., \u0026amp; Atkins, E. (2024). Modelling beyond growth perspectives for sustainable climate futures: the case for rethinking Shared Socioeconomic Pathways. \u003cem\u003eEnergy Research \u0026amp; Social Science\u003c/em\u003e, \u003cem\u003e117\u003c/em\u003e, 103705.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-climate-action","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjclimataction","sideBox":"Learn more about [npj Climate Action](https://www.nature.com/npjclimataction)","snPcode":"44168","submissionUrl":"https://submission.springernature.com/new-submission/44168/3","title":"npj Climate Action","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Integrated Assessment Model, climate equity, energy equity, sustainable development, climate justice, net-zero emissions, modelled pathways, scenarios","lastPublishedDoi":"10.21203/rs.3.rs-7132898/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7132898/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study details a global mitigation pathways model, \u0026lsquo;Model for Energy Equity and Climate Compatibility_Version.1\u0026rsquo; (MEECC_V.1)\u0026rsquo;, with a user interface, using a modelling approach that is a significant departure from all current economy-energy-emissions models, or Integrated Assessment Models (IAMs). It uses economy-wide metrics for projecting socio-economic futures, particularly considering those that are necessary for equity considerations, including convergence of energy use and national incomes across the global North and South and equitable access to the global carbon budget. The modelling approach enables users to explore a range of energy and climate futures instead of determining them based on least cost-optimisation frameworks. The analysis uses a development-based classification of countries as opposed to the commonly used geographical classification for developing countries in current IAMs. Results for a few illustrative scenarios are presented that demonstrate how the model enables the assessment of trade-offs between achieving equitable energy and climate futures.\u003c/p\u003e","manuscriptTitle":"Projected Global and National Energy and Climate Futures using an alternative Integrated Assessment Framework","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 08:18:24","doi":"10.21203/rs.3.rs-7132898/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-28T13:10:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-14T07:51:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305044562209084820968353218717435240045","date":"2025-09-26T06:55:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T15:59:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314093121316085466696794496417409723873","date":"2025-07-31T11:30:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-28T08:03:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-25T08:10:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-23T08:43:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Climate Action","date":"2025-07-15T16:59:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-climate-action","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjclimataction","sideBox":"Learn more about [npj Climate Action](https://www.nature.com/npjclimataction)","snPcode":"44168","submissionUrl":"https://submission.springernature.com/new-submission/44168/3","title":"npj Climate Action","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"111f8554-156f-4e9d-ab2a-0d15c95b2fca","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":52208776,"name":"Earth and environmental sciences/Climate sciences"},{"id":52208777,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2026-03-19T09:24:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-30 08:18:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7132898","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7132898","identity":"rs-7132898","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}