Assessing the Determinants of Performance in Solid Waste Management: An Analysis of Municipalities in Ghana

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The results showed that the average technical efficiency for waste companies in municipalities was 0.79, compared to 0.86 for those in districts. Further analysis revealed that, despite districts spending more on solid waste management per capita (3.833) compared to municipalities (2.53), their waste collection rates were significantly lower, at 0.156 kg/capita/day, compared to 0.42 kg/capita/day in municipalities. Nonetheless, both districts and municipalities fell short of the national average waste collection rate of 0.51 kg/capita/day. A robust multiple linear regression analysis identified organisational structure, cultural and social context as significant determinants of performance. The application of the input-oriented data envelopment analysis technique provides a framework for assessing, monitoring, and benchmarking the performance of waste management companies. Solid Waste Management Data Envelopment Analysis Local Government Organisation Theory Figures Figure 1 Figure 2 Highlights Expenditure per Capita Analysis for Waste Companies Solid Waste Management. Benchmarking Efficient Waste Companies using Data Envelopment Analysis· Comparing Technical and Scale efficiencies of waste companies in rural/urban settings· Variable selection for efficiency determination in solid waste management. Introduction According to the World Bank [1], waste generation is projected to drastically outpace population growth by more than double by 2050. The report estimates that 2.01 billion tonnes of municipal solid waste are generated annually, with at least 33% of that not being managed in a way that protects the environment. Improper management of municipal solid waste (MSW) poses severe ramifications for human beings - cholera epidemics [2] and environmental consequences - annual flooding in many parts of the world is a direct consequence [3, 4]. While municipal authorities recognise the need to formulate comprehensive policies to manage and address sanitation needs adequately, concerns exist about the cost implications [5–7]. More challenging for municipalities and districts in Ghana is not only their inability to formulate solid waste management (SWM) policies but also to develop an objective pathway to evaluate the performance of these policies. Applying subjective measures to determine efficiency often results in disputes and chaos. To ensure standardization of the process, the research was designed the study to: apply the input-oriented data envelopment analysis (DEA) model to measure the efficiency of waste companies in municipal and district areas in Ghana explain the observed variations in efficiency by testing a set of hypotheses. Ensuring efficiency in MSW is more important than ever due to the rising management costs. Karak et al. [8] explain that SWM constitutes the single highest budget item for many local administrations in low-income countries, comprising nearly 20% of municipal budgets, 10% for middle-income countries, and 4 % in high-income countries on average [9]. Other researchers also argue that, besides the cost, limiting the implementation of MSMW plans, there are issues related to the politicisation of sanitation issues and an inadequate expertise in the management process [10–12]. Waste companies in rural and urban areas face significant challenges at various stages of waste management. Those in the cities struggle the most, given the population increase in municipal centres due to the exodus of people from rural communities. This situation has created a compelling need to develop a mechanism to manage solid waste in Africa [9, 10]. The amount of waste generated worldwide varies disproportionally across different regions. According to Zicha et al. [13], 35% of the world's waste comes from countries with high Gross National Income, such as Australia, Canada, Denmark, and the United States (USA). In contrast, Sub-Saharan Africa (AFR) has the lowest generation rate, 0.47 kg/capita/day, representing 6% of global waste [9]. Malinauskaite et al. [14] also estimate that, as of 2015, the average amount of municipal solid waste (MSW) generated annually by each of the approximately 512 million inhabitants of the European Union was 477 kg per capita (see Eurostat report, 2017). In contrast, Ghana's amount is controversial because of the need for more data. While several studies report that SW generation is around 12,710-13,000 tons [15–18], the World Bank [1] indicates that Ghana generates 0.51kg/capita/day. This figure is higher than the Sub-Saharan Africa average of 0.46kg per capita/day but less than the world average of 0.74 kg per day/ person. Developing a policy framework is the first step in the management process [13, 19, 20]. Developing a comprehensive policy framework or plan also requires a complete understanding of waste characterisation [21,22]. Characterisation determines the management strategy and allowable activities in an economy, from industry and agriculture to transportation. Waste audit studies indicate that food and green waste account for 44 per cent of global waste. Dry recyclables (plastic, paper and cardboard, metal, and glass) amount to another 38 per cent of garbage [1]. According to Miezah et al. [23], the composition of SW in Ghana is 61% organic, 14% plastic, 6% inert, 5% miscellaneous, 5% paper, 3% metals, 3% glass, 1% leather and rubber, and 1% textiles. Unlike other developing countries, Ghana has a robust regulatory framework, the revised environmental sanitation policy (ESP) (2010), which governs waste management. The policy assigns full responsibility for waste management to local government authorities (see Adzawla et al. [16]. The focal areas included in the revised ESP (2010) [60] are capacity development, information, education and communication, legislation and regulation, service levels, sustainable financing and cost recovery, research and development, and monitoring and evaluation management activities challenge [15]. While this waste management infrastructure appears solid on paper, authorities struggle to find a mechanism for implementing and evaluating these policies. This situation is unsurprising because, according to Bel et al. [24], municipal solid waste (MSW) collection and disposal are among the most challenging services for local authorities. This complexity often overwhelms city authorities [24–28]. A common strategy adopted by municipalities in Ghana in dealing with SWM is to change the service arrangement management mechanism from the historically dominated public domain to private or from one private vendor to another [29]. However, this strategy has not yielded efficiency because waste collection statistics across the globe show that only high-income countries, particularly those in Europe and North America, are nearing 100% collection. Lower-middle-income countries account for approximately 51 per cent, and 39 per cent of low-income countries for [30]. More worrying is that about 95% of the 39% of solid waste collected in developing countries is not recycled [30]. Applying data envelopment analysis (DEA) to this study provides objective criteria for evaluating the efficiency of solid waste management across a set of municipalities and districts in Ghana. This approach enables the identification of an efficient waste company at both the municipal and district tiers in SWM, serving as a benchmark for other competing companies. Additionally, performing statistical analysis on contextual variables identified factors explaining variations in performance. Overall, the study framework provides a systematic approach for waste companies to estimate technical and scale efficiency by comparing expenditure per capita to waste collection per capita per day, thereby assessing efficiency. In this study, the terms "performance" and "efficiency" are used interchangeably, although they have distinct meanings. Efficiency in the context of Data Envelopment Analysis (DEA) was defined through the prism of input minimisation [61]. In contrast, the study defined performance as the ability of waste companies to achieve broader organisational goals, specifically delivering equitable economic and social services in local government areas [62]. The research employs archival data from rural and urban districts/municipalities in Ghana plagued by waste management problems. Our study provides an objective pathway for assessing local governments' policies in an environment of unique cultural settings, where finding resources to deal exclusively with solid waste is challenging because of crowded out other social services in health and education, among other areas. This situation is exacerbated by central governments' tendency to devolve waste expenditure responsibilities to municipalities without providing commensurate funding, thereby straining local budgets [10] The paper is laid out as follows. Section 2 discusses the determinants of performance in solid waste management. Section 3 describes the institutional framework of SWM in Ghana. Sector 4 describes the research design, and Section 5 discusses the research methodology, data, and model selection. Section 6 describes the estimation procedures and the results of the DEA analysis. The presentation of the results of the determinants of performance in Section 7 follows the above section. Finally, section 8 presents the discussion. The article closes with the conclusions, policy implications and areas of further research. Literature Review Determinants of Performance Measures of Solid Waste Management Managing solid waste is a complex task requiring strategies to improve technical efficiency, i.e., achieving a desirable output with the least input costs while addressing health concerns [ 31 , 32 ]. While the Data Envelopment Analysis (DEA) model yields a single efficiency score for evaluating waste companies performance, it falls short in providing deep insights into the observed variations in technical and scale efficiencies between municipalities (technical efficiency: 0.95; scale efficiency: 0.97) and districts (technical efficiency: 0.87; scale efficiency: 0.76). This study collected data on relevant variables informed by a comprehensive literature review, the existing service delivery structure, and the regulatory framework governing solid waste management in Ghana. The aim was to identify explanatory variables that the Data Envelopment Analysis (DEA) could not capture in the first analysis stage to provide further insights into the efficiency drivers. Ghana has a robust institutional framework for solid waste management, which is governed by the Revised Environmental Sanitation Policy, developed in 2010. The policy harmonised waste and sanitation policies that were scattered in piecemeal legislations. The policy has several guidelines for dealing with integrated waste management, such as the National Environmental Quality Guidelines, 1998; Ghana Landfill Guidelines, 2002; Manual for the Preparation of District Waste Management Plans, 2002; Guidelines for Bio-medical Waste, 2000; Guidelines for the Management of Healthcare and Veterinary Waste, 2002; District Level Environmental Sanitation Strategy and Action Plan (DESSAP), and the National Environmental Sanitation Strategy Action Plan (NESSAP) enacted to control waste and sanitation management decisions that were being applied by various national bodies [ 33 ]. The approach that metropolitan, municipal, and district assemblies (MMDAs) adopt for solid waste management varies significantly across different areas. While some entities outsource management to private companies, others utilise house mechanisms. The overarching framework guiding service delivery in waste management is illustrated in Fig. 1 . There are several layers of SWM in Ghana. The Environmental Protection Agency (EPA) and Ministry of Local Government and Rural Development (MLGRD) are two upper-tier ministries within the waste management architecture that formulate policy and coordinate and monitor the implementation of SWM activities and programs to be consistent with the tenets of Ghana’s Revised Environmental Sanitation Policy 2010 (Ministry of Local Government and Rural Development, 2010). Metropolitans, Municipalities, and District Assemblies (MMDAs) are the local authorities that implement policies through an in-house, private, or joint model. Marshall and Farahbakhsh’s [ 34 ] study applied this regulatory framework to examine performance based on organisational theory. Their study argued that performance in SWM depends on organisational structure, institutional service delivery arrangement, and socio-cultural variables. A substantial body of literature highlights the impact of contextual variables on the efficiency of solid waste management (SWM) [ 35 , 36 ]. This is exemplified by Struk and Boďa’s [ 37 ] study of Czech municipalities, which identified 12 influential factors, including population density, housing spatial arrangements, bin records, and waste separation incentive programs. Based on local dynamics and the existing literature, the study identified five variables, described in further detail in the variable selection section, as relevant predictors. They included demographic characteristics, tasks and organisational structure of each waste company, motivation (reward mechanisms to incentivise employees), socio-cultural behaviour of the people in the area, and the politicisation of SWM policies. The ISWM assumes that citizens are not mere consumers, but rather that public officials/ bureaucrats design and provide services to satisfy [ 38 – 40 ]. The framework views citizenry as an essential component in formulating and implementing SWM policy, without whom the policy will fail. This concept aligns with the objective of examining the determinants of performance from a multivariate approach: technical, social, cultural, and institutional contexts [ 5 , 16 ]. Thus, our hypothesis that: H 1 : Municipalities where citizens adopt behavior tendencies consistent with existing waste policies will be efficient in managing solid waste. The technical perspective argues that most public policies on solid waste management fail at the implementation stage because the personnel charged with SWM implementation responsibilities have low educational levels and limited specialised training, particularly in developing countries [ 41 ]. Due to their low academic qualifications, salaries are also low, which affects their motivation and commitment to work. The study further argues that the new waste management trend requires in-depth knowledge of pluralistic policy and an understanding of the role of multiple interdependent actors in public management [ 42 ]. Inadequate skills or a lack of personnel in SWM will create a gap between policy and implementation. Thus, H 2 : Employing workers with higher education levels and skills will positively impact solid waste management. Adzawla et al. [ 16 ] employ a behavioural perspective to explain that the problem facing developing countries, particularly local government authorities in Ghana, in disposing of solid waste is attitudinally or demographically related rather than skill- or income-related. Therefore, institutional measures designed to create acceptable behaviour will reduce the complexity of the social disorder. Given this rationale, our study examined the performance of waste utility companies in an environment of behaviour control measures such as government regulations, taxes, and the encouragement of new technologies in SWM. Regulation determines waste characterisation, management techniques, and allowable activities ranging from agriculture to industry and transportation [ 13 , 22 ]. The study, therefore, argues that institutional measures, ranging from a code of ethics and sanitation laws to offering something of material, social, or normative value to sanitation employees and the citizenry, prompt them to act as co-producers in SWM [ 43 ]. H 3 : Municipalities that have instituted sanitation bylaws and regularly update their solid waste management plans will be efficient. H 4 : Municipalities with reward systems to motivate employees and encourage good citizen behaviour will be efficient. Research Design and Methods This study uses a cross-sectional design to assess the efficiency of the SWM of municipalities in Ghana for the fiscal year 2022–2022. The study covered five geographical regions as shown in Fig. 2 . The locations selected in districts and municipalities in Ghana faced issues of increasing urbanisation and high levels of economic activities accompanied by high solid waste generation. These issues impose significant difficulties for SWM companies. The research methodology employed in this study consisted of a two-stage approach. The first stage involved the application of the data envelopment analysis (DEA) to conduct an efficiency analysis, with the primary objective of assessing the performance of waste management companies operating within municipal/district jurisdictions. The research computed efficiency using the ratio of the number of skip containers and compactors, cost data of inputs - operational cost (OPEX)- current expenditure, and cost of labour, gathered from municipalities' budgets, quarterly reports, and annual audited accounts for the 2021–2022 fiscal year, to the yearly tons of solid waste, which was estimated using the number and size of utilities' skip, compact, and roller containers [ 44 ]. In the second stage, a robust multiple linear regression analysis was conducted to ascertain how the data confirms the theory. The independent variables selected for the analysis consisted of organisational structure, institutional arrangements, social and cultural context, and the dependent variable was the efficiency scores obtained in the first stage. These study variables were based on the research context and the literature on SWM, which is described in detail in the variable selection section [ 10 , 45 , 46 ] Application of the DEA Technique Our study applied IBM's statistical software package "Stata17\StataSE-64.exe" to run the results for practical purposes. A mathematical procedure described section in Appendix A section shows the theory and the computational script for evaluating the relative efficiency of waste utilities. The tool is configured to run the results using the input orientation DEA Model by default. Also, preceding running the DEA results is the data processing stage, which essentially refers to configuring the raw data (inputs and outputs) from the field into a format compatible with the software. Table 1 shows the raw field data preprocessed for the DEA analysis. DEA Inputs and outputs Selection This study's inputs consisted of Operational expenditure OPEX (Salary + Fuel + Electricity + Repairs), cost of labour, and the number of skip containers and compactors gathered from the budgets of districts and municipalities for the 2020–2021 fiscal year. There was, however, no data on the amount of solid waste generated, as is the case for most local governments [ 44 ]. Therefore, data on the annual tons of rubbish each municipality generates were obtained transitively using the size of the containers and the weekly collection cycle. To Illustrate: 1. Skip = 220 metric tons, 1 Roll-on = 250 metric tons, 1 compactor = 250 metric tonnes. The total metric of rubbish produced for the fiscal year 2020–2021 was estimated based on each municipal truck's weekly trips to landfill sites. A summary of the data is shown in the Table 1 . Rationale for Input-Oriented DEA Theoretical Model The rationale for applying the input-oriented DEA model which is theoretically grounded in the literature Rogge and De Jaeger [ 25 ]; Pérez-López et al. [ 47 ]; Expósito and Velasco [ 48 ]; Struk and Boďa [ 37 ]) to estimate the efficiencies and rank waste companies lies in the study's objective of input minimization- seeking to achieve the maximum proportional reduction in input usage that is compatible with the technology set [ 49 ]. By applying this technique, enabled us to establish an efficiency frontier of high-performing companies to serve as benchmarks to measure the relative efficiency of average and non-performing companies. Consistent with the existing literature [ 26 , 50 , 51 , 53 ], the input-oriented DEA model proved effective in comparing the performance of waste companies in Ghana, particularly in an operational environment characterized by limited information on input and output prices [ 44 ]. This efficiency approach promotes economic rationality, which can help Ghanaian municipalities optimize resource allocation and reduce unnecessary expenditures. Results and Analysis Table 1 below presents the input data, consisting of operational expenditure (OPEX), the number and size of skips, rollers, and compactors used to generate the annual tons of solid waste collected by each municipality. By applying IBM's statistical software package, Stata 17 (StataSE-64.exe), our study conducted the input-oriented model DEA model to determine the efficiency score, as shown in Table 2 , i.e., a comprehensive overview of the relative performance of each waste company, from technical to scale efficiencies, as well as returns to scale, which indicate whether utilities are operating under increasing or decreasing returns to scale. To ensure a practical comparative analysis, waste companies were grouped by district and municipal tier levels to compare technical and scale efficiencies, as shown in Table 3 . A score of one demonstrates the highest efficiency on an efficiency scale of 0–1. The analysis also focused on the expenditure per capita that each company incurs to collect waste and the daily kilogram of waste collected, providing more insight into the cost efficiency analysis, as shown in Table 5 . The waste estimation model outlined in Table 1 offers a reliable framework for local authorities to quantify waste generation in municipalities and districts thereby overcoming the limitation in the non-available of data. By providing accurate estimates of daily waste production, this model can inform effective waste management planning and policy-making. The ability to estimate the amount of waste generated daily enabled us to determine the expenditure per capita and the amount of waste produced in kilograms per capita per day, as described earlier in Table 5 . Using this estimation approach, the study was able to make statistical inferences on whether a waste company was utilising more or fewer resources in the collection of solid waste, as shown in Fig. A2 of Appendix A. Table 1 Solid waste management data for municipalities districts in Ghana for the 2021–2022 period Municipality/ District Capitals Number of Employees Number of skip Containers Number of Compactors Operational Expenditure (OPEX) Yearly Estimate in metric tons AGONA EAST 220 13 0 256690 17,600 AGONA WEST 296 13 0 693800 22,880 ASSIN SOUTH 211 14 0 326900 24,640 ASSIN NORTH 173 11 0 406800 19,360 AJU_ ENY_ ESS 165 9 0 425300 15,840 ASIK_ODOB 175 12 0 598000 21,120 BIBIANI 190 15 1 294060 40,600 WIAWSO 174 14 1 367850 11,560 JUABOSO 122 8 0 298470 14,080 AKONTOMBRA 300 6 0 1202500 10,560 BODI 60 13 0 276690 7,040 BIA EAST 100 8 0 526500 14,080 BIA WEST 80 6 0 499200 10,560 SUAMAN 96 7 0 419400 12,320 AOWIN 105 11 0 345500 19,360 AMENFI WEST 165 13 0 410000 21,120 AMENFI CENTRAL 105 8 0 228000 14,080 ATW_ MPO 374 11 0 387000 19,360 EJISU 160 14 1 366758 10,910 ASANTE BEKWAI 250 17 1 184756 30,920 ASE_ MAN_ AKR 12 11 0 141924 2,420 GOASO 120 13 1 301000 23,880 AHANTA WEST 120 13 1 230690 12,440 TARKWA MUNI 298 21 1 602231 37,960 UPP_ DEN 78 7 0 179000 12,320 WASSA EAST 138 11 0 269760 24,200 Table 1 shows annual data for inputs, OPEX, labour, number of solid waste collection objects (compactors, skip containers) used to estimate annual tons of solid waste in each municipality. Data collected manually from archival records of MMDAs for the year ending 2022. Table 2 Efficiency values for Waste Companies on VRS and CRS MUNICIPALITIES CRS_TE VRS_TE NIRS_TE SCALE RTS AGONA_EAST_ 0.500189 0.726424 0.590503 0.688564 1.000000 AGONA_WEST 0.650246 0.745468 0.739140 0.872266 1.000000 ASSIN_SOUTH_ 0.650246 0.747278 0.674105 0.870153 1.000000 ASSIN_NORTH_ 0.650246 0.800932 0.679612 0.811862 1.000000 AJU__ENY__ESS 0.650246 0.859095 0.687556 0.756897 1.000000 ASIK_ODOB 0.650246 0.763648 0.753602 0.851500 1.000000 BIBIANI 1.000000 1.000000 1.000000 1.000000 0.000000 WIAWSO 0.310911 0.498985 0.325325 0.623087 1.000000 JUABOSO 0.650246 0.920265 0.687184 0.706586 1.000000 AKONTOMBRA 0.650246 1.000000 1.000000 0.650246 1.000000 BODI 0.549097 0.708374 0.624049 0.775151 1.000000 BIA_EAST 0.658916 0.901201 1.000000 0.731153 1.000000 BIA_WEST 0.650246 1.000000 1.000000 0.650246 1.000000 SUAMAN 0.650246 0.951757 0.823431 0.683206 1.000000 AOWIN 0.862867 0.952597 0.986922 0.905804 1.000000 AMENFI_WEST 0.600227 0.720446 0.648121 0.833133 1.000000 AMENFI_CENTRAL 0.650246 0.932178 0.678954 0.697556 1.000000 ATW__MPO 0.650246 0.803030 1.000000 0.809741 1.000000 EJISU 0.319104 0.499097 0.336673 0.639363 1.000000 ASANTE_BEKWAI 1.000000 1.000000 1.000000 1.000000 0.000000 ASE__MAN__AKR 0.943760 1.000000 1.000000 0.943760 1.000000 GOASO 0.931281 0.959276 1.000000 0.970816 1.000000 AHANTA_WEST 0.485140 0.736416 0.486655 0.658785 1.000000 TARKWA_MUNI 0.667840 0.676622 1.000000 0.987021 1.000000 UPP__DEN 0.739169 1.000000 0.778339 0.739169 1.000000 WASSA_EAST_ 0.820661 0.938286 0.847917 0.874639 1.000000 Table 2 shows Scale Efficiency(SE), Return to Scale (RTS), ranking of Technical efficiencies (TE) for Waste Companies on CRS and VRS. VRS Frontier (-1: drs, 0: crs, 1: irs) Table 3 Efficiency comparison for waste companies in districts/municipalities Districts Category Technical Efficiency Scale Efficiency AGONA_EAST_AGONA_AST 0.726424 0.688564 ASSIN_SOUTH_ 0.747278 0.870153 AJU__ENY__ESS 0.859095 0.756897 ASIK_ODOB 0.763648 0.851500 JUABOSO 0.920265 0.706586 AKONTOMBRA 1 0.650246 BODI 0.708374 0.775151 Bia East 0.901201 0.731153 BIA_WEST 1 0.650246 SUAMAN 0.951757 0.683206 AOWIN 0.952597 0.905804 AMENFI_CENTRAL 0.932178 0.697556 ATW__MPO 0.803030 0.809741 ASE__MAN__AKR 1 0.943760 AHANTA_WEST 0.736416 0.658785 UPP__DEN 1 0.739169 WASSA_EAST_ 0.936267 0.874639 Average 0.878737 0.764303 Municipal Category Technical Efficiency Scale Efficiency AGONA_WEST 0.745468 0.872266 ASSIN_NORTH_ 0.800932 0.811862 BIBIANI 1 1 WIAWSO 0.49898 0.623087 AMENFI_WEST 0.720446 0.833133 EJISU 0.499097 0.639363 ASANTE_BEKWAI 1 1 GOASO 0.959276 0.970816 TARKWA_MUNI 0.676622 0.987021 UPP__DEN_East 1 0.739167 Average 0.790082 0.847394 A more detailed graphical representation is also shown in Fig. A1 of Appendix A. Variable Selection to Explain Variations in the Efficiency of Waste Management Companies Following the efficiency assessment, an investigative process was conducted to determine the factors underlying the observed variations in technical and scale efficiencies among the waste companies. This systematic and comprehensive literature review was performed to identify indicators impacting performance using Web of Science (webofscience.com), Google Scholar (scholar.google.com), and other databases. This search technique applied several variant keywords relating to 'performance', 'efficiency', and ‘solid waste’, yielding a large pool of articles from the literature. The identified indicators from these papers were subsequently organised under thematic categories, encompassing the legal and institutional framework, cultural contex, social context, and technical & organizational structure, and institutional arrangement health pertinent to SWM in Ghana. The study applied this categorisation given the unique environment for solid waste management, as determined in Fig. 1 . The criteria for selecting and excluding performance indicators was based on Trawick’s [ 52 ] framework for selecting indicators: relevance to scope, measurability, performance-based, scientific robustness, comparability, data quality, data availability, broad acceptance, compliance, consensus-based, clarity, and transparency. The third stage after selecting the predictors involved gathering data related to these variables through qualitative surveys. The final stage involved performing a robust multiple linear regression to check whether the data confirms the theory. The categorical variables selected from this process consisted of technical -academic qualification and training, institutional arrangements, social, and cultural context. Table 4 presents the variable descriptions, measurement items, and scales of measurement. These variables were grouped as independent variables impacting performance (PQ), whose value was obtained from the DEA analysis in the first stage. Table 4 Description of performance determinants Variable Technical & organizational structure Institutional Arrangement (IA) Social context) (SC) Cultural Context (CC) Description Technical or job competencies acquired through education [ 41 ]. Design and Implementation of regulatory regime [ 13 , 22 ] Rewards for regulatory compliance. Verschuere et al. [ 43 ], Normann [ 38 ]) Describes the attitudinal or demographic text affecting waste management Adzawla et al. [ 16 ] Items of measurement Education level, specialized training, salary appreciation Ethics and code of conduct, sanitation bylaws Fees, taxes, punishments, incentives, etc. burning of waste, littering around, waste containers management, etc. Scale The items were rated on a five-point Likert scale: 1, strongly disagree; 2, disagree; 3, Prefer not to answer; 4, agree; 5, strongly agree The items were rated on a five-point Likert scale: 1, strongly disagree; 2, disagree; 3, Prefer not to answer; 4, agree; 5, strongly agree The items were rated on a five-point Likert scale: 1, strongly disagree; 2, disagree; 3, Prefer not to answer; 4, agree; 5, strongly agree The items were rated on a five-point Likert scale: 1, strongly disagree; 2, disagree; 3, Prefer not to answer; 4, agree; 5, strongly agree Table 5 Municipalities/districts Operational expenditure, expenditure/capita & waste/kg/day Municipalities Population Expenditure Expenditure /capita Yearly Tons *10 6 Kg Kg/Capita/day BIBIANI 167,971 294,060 1.750 40,600 40.6 0.66 SEFWI WIAWSO 151,220 367,850 2.433 11,560 11.56 0.21 AGONA WEST 136,882 693,800 5.07 22,880 22.88 0.45 ASSIN NORTH 161,341 406,800 2.52 19,360 19.36 0.33 GOASO 150,198 301,000 2.004 23,880 23.88 0.44 AMENFI_WEST 129,882 410,000 3.16 21,120 21.12 0.45 EJISU 180,723 366758 2.029 10,910 10.91 0.17 ASANTE BEKWAI 137,967 184,756 1.339 30,920 30.92 0.61 TARKWA NSUAYEM 218,664 602,231 2.75 37,960 37.96 0.48 Average 159,428 403,028 2.53 24,354 24.354 0.42 Districts Population Rec/Exp Exp/Capita Tons *10 6 Kg Kg/Capita/day AGONA EAST 98324 256690 2.61 17,600 17.6 0.18 ASSIN SOUTH 105995 326900 3.08 22,880 22.88 0.22 AJU_ENY_ESS 120586 425300 3.53 15,840 15.84 0.13 ASIKUMA_ODOBEN 126993 598000 4.71 21,120 21.12 0.166 JUABOSO 70225 298470 4.25 14,080 14.08 0.20 AKONTOMBRA 98324 1202500 12.23 10,560 10.56 0.11 UPPER_DEN_WEST 91324 179000 1.96 12,320 12.32 0.14 BIA_EAST 53073 526500 9.92 14,080 14.08 0.27 BIA_WEST 115881 499200 4.31 10,560 10.56 0.09 SUAMAN 38268 419400 10.96 12,320 12.32 0.32 ATWIMA_MPONUA 155254 387000 2.49 19,360 19.36 0.12 AHANTA_WEST_ 153140 230690 1.51 12,440 12.44 0.08 BODI 65748 276690 4.28 7,040 7.04 0.11 AMENFI CENTRAL 119117 228000 1.91 14,080 14.08 0.12 ASENE MANSO AKROSO 77498 141924 1.83 2,420 2.42 0.03 WASSA EAST 105000 269760 2.57 24,200 24.2 0.23 AOWIN 129721 345500 2.66 19,360 19.36 0.15 Average 101439.47 388913 3.833 14,721 14.72 0.156 Table 5 shows the expenditure the per capita and the amount of waste collected by companies in municipalities and districts. The results of the descriptive statistics, multivariariate and the correlation analysis are presented in Table 6 . Table 6 Descriptive Statistics and Multivariate Analysis VARIABLES N Mean sd Min Max Performance 26 0.84 0.15 0.49 1.00 Technical & Organizion Structure 26 3.92 1.05 1.00 5.00 Institutional arrangement 26 3.46 1.06 1.00 5.00 Social context 26 3.96 0.92 1.0 5.00 Cultural context 26 4.07 1.26 1.0 5.00 MODEL RESULTS VARIABLES Model 1 Organizational structure 0.015* (0.009) Institutional Arrangements 0.060 (0.009) Social Context 0.004** (0.01) Cultural context 4.93e-08 *** (0.01) Constant 5.97e-06 *** (0.04) Observations 26 Table 7 Correlation coefficients between variables Performance Technical & organizational structure Institutional Arrangement (IA) Social context (SC) Cultural Context (CC) Technical & organizational structure 0.56 1.00 0.46 0.12 0.51 Technical & organizational structure 0.80 0.45 1.00 0.63 0.80 Institutional Arrangement (IA) 0.74 0.12 0.63 1.00 0.69 Cultural Context (CC) 0.94 0.51 0.80 0.69 1.00 The correlation is significant at *0.005 Discussion This two-pronged discussion is grounded in the findings of the Data Envelopment Analysis (DEA) conducted in Stage 1 to determine the efficiencies of waste companies and the robust multiple linear regression on contextual variables aimed at elucidating the factors underlying the observed variations in efficiency. The results show that the technical efficiency for waste companies in the districts was 0.87 compared to 0.79 for those in the municipalities. If technical efficiency θ* < 1, then 1 − θ* represents the maximum radial reduction of the inputs, which will still ensure that the given outputs are producible for a waste company [ 53 ]. The objective function of this model is to find a minimal value for an 'intensity' factor \(\:\theta\:\) which corresponds to the waste utility’s ability, or the potential for a proportional reduction in all the inputs [ 54 ]. Except for Akontombra, Asene Manso Akroso, Bia West, Upper Denkyira West and East, and Bibiani, and Asante Bekwai, all the waste companies failed to achieve θ* = 1, indicating the presence of slacks in their operations, as shown in Fig. A2 in the Appendix A. Also, the study found that waste companies in municipalities had a scaling efficiency of 0.85 compared to 0.76 for districts. This indicates that municipal waste companies could focus on scale efficiencies while those in smaller communities’ work on improving technical efficiencies. This result also demonstrates that scale efficiency tends increase as the size of infrastructure increase. There is an unsettled view in the literature regarding the relationship between a company’s size and its efficiency. While the inverse relationship between scale efficiency and municipal infrastructure size is a well-established finding in the literature (Brettenny & Sharp [ 53 ], other studies show conflicting findings. For instance, Guerrini et al. [ 35 ] suggest a positive association between technical efficiency and size, whereas Berg and Marques [ 55 ] reveal a negative relationship. Although the average technical efficiency for waste companies in municipal and district areas is better than generally believed, an individual examination shows that many are cost-inefficient and operate below the 75% quartile in solid waste collection. Notably, Ejisu, Sefwi Wiawso, and Tarkwa Nsuayem as indicated in Fig. A2 in the Appendix A. This pattern is consistent with the study of Rogge and De Jaeger [ 25 ] for municipalities in Flanders, Belgium. Further analysis, as shown in Table 2 demonstrates that municipalities produced 0.264kg/capita/day more solid waste than districts for the fiscal year 2021–2022. In other words, waste collection for companies in the municipalities was 0.42 Kg/Capita/day compared to 0.156Kg/Capita/day for companies in the districts. Although 0.42 Kg/Capita/day is far higher compared to the districts, it is lower when compared to the average Ghana value of 0.51kg/capita/day and the Sub-Saharan regional average of 0.46kg/capita/day [ 1 , 5 ]. The relatively small average value for the district does not also suggest an improvement in the sanitation situation, as most of the waste in the rural areas is openly dumped and burned and, therefore, does not even find its way into the waste bins for collection [ 56 ]. Furthermore, a comparative analysis of expenditure per capita for waste collection revealed that municipal companies incurred a lower cost of 2.53, whereas rural companies expended 3.833. Conversely, municipal companies collected a higher amount of waste, 0.42kg/capita/day, compared to the 0.156kg/capita/day collected by their rural counterparts. This trend suggests a significant waste collection efficiency and expenditure disparity between these two contexts. While statistical inference from the DEA analysis results [ 28 ] can be used to determine the companies' inputs and outputs, the technique does not provide insight into the underlying factors driving the variations in performance among the waste companies, as observed in the first stage of the DEA analysis. To explain the observed variations in efficiency among the waste companies, categorical variables were identified using Trawick’s [ 52 ] framework . The framework allowed us to select and group variables that can impact performance based on a literature search, the legal framework for waste delivery in Ghana, and other contextual factors. In the initial stages of our analysis, we checked the data's normality using the Shapiro-Wilk test [ 58 ] to inform the choice of the statistical test. The test revealed that the data was not normally distributed and that outliers were confirmed by the negatively skewed value of -0.4575038. Our study, therefore, utilised the robust multiple linear regression (RMLR) analysis method due to the limited sample size, where outlier detection is essential to ensure the accuracy and reliability of the results. The robust multiple linear regression enabled us to predict the collective effect of all the variables and determine the impact of each of the variables on performance. Overall, the variables showed varying correlations among themselves. For instance, as shown by Table 7 , there were strong positive correlations between Institutional arrangement and cultural context (0.80). Social context and institutional arrangements showed a moderately strong correlation (0.63) compared to (0.51) for Task and organisational structure and culture. Social context and Organisational structure showed the weakest correlations of 0.12, suggesting they were not asynchronous. A computed standard Cronbach's alpha of 0.82 also shows a reasonable internal consistency of the scale used to measure the items. Again, it also indicates that the items share a substantial degree of covariance, reflecting a reasonable level of reliability in measuring the underlying construct. The analysis of the robust multiple linear regression revealed that while some variables had a positive and significant impact on performance, others showed a positive but insignificant impact. For instance, the results indicate that performance is positively related to the social arrangements that waste companies adopt in their waste management services (t = 3.193, p = 0.004). The implication is that rewards and offering material incentives can impact people's behaviour in a community, which can significantly impact waste management performance. The conclusion is that waste companies operating in municipalities and districts where citizens tend to adopt behaviour patterns consistent with existing waste policies are more efficient in managing solid waste, as supported by the literature [ 13 , 43 ]. However, the results contradict previous studies on Ghana by Agbefe et al. [ 2 ] and Adzawla et al. [ 16 ], which attribute low performance in SWM to urbanisation and poor collection regimes. Again, the results show that the impact of institutional measures on performance was not significant, indicating that waste companies lack an incentive structure within their operational policies to regulate and encourage hard work and personal agency. On the one hand, the results also show that performance depends on the nature of sanitation employees' tasks, given their related educational qualifications. (t = 2.62, p = 0.015). Given the significant impact of Technical skill upgrade on performance in an organisation with a well-defined structure, it follows that employing workers with higher education levels and technical skills will positively impact solid waste management. This result is consistent with the study of Buenrostro and Bocco [ 41 ], which established a positive relationship between higher educational performance. Our study results also show that cultural variables, such as people's behaviour, significantly impact a municipality's performance in SWM. Although the results demonstrate that the amount of waste generated and collected by companies in the cities far exceeds districts, considering that city areas have issues with higher population densities [ 3 , 59 ], the latter was more cost-efficient, with average expenditure per capita for solid waste being 3.8GHc compared to 2.5GHc for utilities in the cities. Conclusion The two-stage research framework employed in this study provides a systematic and objective approach for city authories to analyse waste companies' performance and estimating the amount of waste that local governments generate. Furthermore, this framework sheds light on the causes of variations observed in their performance. By utilising this framework, researchers and policymakers can gain valuable insights into the complexities of waste management and develop more effective strategies for improvement. The waste estimation approach should solve the problem of the lack of data on the amount of waste generated in rural and urban areas, which arguably affects policy planning and implementation [ 44 ]. The study shows that, on average, waste companies in municipalities were technically more efficient (0.87) compared to those in districts (0.79). Optimising efficiency requires that waste companies in the municipalities to radially reduce inputs 1- θ or by 13% whereas companies in the districts need to reduce their inputs by 21% to be efficient [ 53 ]. The study found that while the expenditure per capita for waste collected by municipalities (3.45) was lower than that for utilities in the districts (3.833), the amount of waste collected was 0.156kg/capita/day, compared to 0.42kg/capita/day for companies. The expenditure per capita analysis can provide city authorities with the information needed to identify slack variables and make informed changes. The measurement of the scale efficiency gave the study an idea of the source of inefficiency, i.e., increasing or decreasing returns to scale. For example, the scale efficiency results showed that except for the waste company in Asante Bekwai, whose source of scale efficiency was due to decreasing returns to scale- production of an inefficiently large amount of output, most companies in districts and municipalities showed increasing returns to scale – applying massive inputs to produce an inefficiently small amount of output. Our study presents a systematic approach for selecting variables that positively or negatively impact waste management performance. This framework enables managers to identify contextual variables that impact the provision of waste management services in local areas, or adjust policies, particularly when dealing with variables outside their control. Overall, the results demonstrate that social context has a significant impact on performance, confirming the theory that performance in waste management is determined by the social structure, such as sanitation bylaws and motivation to encourage good citizen behaviour [ 13 , 43 ]. However, the impact of institutional arrangements further demonstrates that waste companies' operations in local government areas lacked institutional measures to motivate and regulate sanitation workers, thereby showing an insignificant impact on performance. Additionally, the results show that the tasks employees perform, given their related educational qualifications, significantly impact performance [ 41 ]. Furthermore, the results revealed cultural aspects, i.e., people's attitudes and behaviours [ 16 ], as significant performance determinants, as confirmed by the theory of performance and cultural antecedents [ 16 ]. Limitations and future research One constraint of this study, similar to numerous others, is the accessibility and reliability of the data. Despite Ghana enacting the Right to Information Act, seeking institutional data for academic purposes was seen as a potential effort to uncover misconduct. The research tackled this challenge by utilizing excerpts from secondary sources, including independent entities such as parliament and the audit service. Our research concentrated on annual data, which restricts our ability to observe efficiency trends over time. The research suggests utilizing the Malmquist productivity index [ 64 ] and additional methods like the bootstrap [ 63 ] in conjunction with the DEA technique to assess efficiency across several fiscal years. By implementing these strategies, future studies can expand on our results and offer a deeper comprehension of efficiency within the solid waste management to provide a more nuanced understanding of efficiency in the SMW. Another area of future research is the application of the study methods to examine the inconsistency between the impact of institutional arrangements and SWM using data from other communities. Declarations Acknowledgements This research was made possible through the technical advice and supervisory direction of Prof António Fernando Tavares Associate Professor w/ Habilitation in Political Science School of Economics and Management University of Minho 4710-057 Braga, Portugal. Webpage: https://sites.google.com/site/umpianonafloresta/home; URL: https://www.eeg.uminho.pt/en/_layouts/15/UMinho.PortaisUOEI.UI/Pages/userinfo.aspx?p=1925. Funding : No applicable. Competing interests : The authors have no relevant financial or non-financial interests to disclose. Availability of data and material: All data generated or analysed during this study are included in this published article [and its supplementary information files] Code availability: Not applicable Authors' contributions : All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Moses Kofi Armah and Miguel Ângelo Vilela Rodrigues. The first draft of the manuscript was written by Moses Kofi Armah and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Not applicable. Ethics approval : Not applicable. Consent to participate : Not applicable. Consent for publication : Not applicable. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7723092","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":528814970,"identity":"0348523b-517a-4281-9bc9-229d3e25ecc9","order_by":0,"name":"Moses Kofi 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Vilela","lastName":"Rodrigues","suffix":""}],"badges":[],"createdAt":"2025-09-26 15:17:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7723092/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7723092/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s43621-026-02793-x","type":"published","date":"2026-04-16T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93557100,"identity":"861ab7f4-b8e0-432e-a8ee-9abe44542033","added_by":"auto","created_at":"2025-10-15 06:48:23","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":344442,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7723092/v1/413b67ddf2ee715cbd6c43c3.docx"},{"id":93557099,"identity":"2d0bac53-b8ed-4a54-9c47-f3ad76f4b3f0","added_by":"auto","created_at":"2025-10-15 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06:48:23","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":217422,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7723092/v1/c09c9724be698ca78b0aaab0.html"},{"id":93557098,"identity":"a57595cc-6599-4e5a-9608-8a9a5d04c583","added_by":"auto","created_at":"2025-10-15 06:48:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":14838,"visible":true,"origin":"","legend":"\u003cp\u003eSolid waste management flow chart\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7723092/v1/a9bbd0889b35dd524ca3faf7.png"},{"id":93558219,"identity":"68057dbb-da35-4a29-9e2a-b83ddce71b97","added_by":"auto","created_at":"2025-10-15 06:56:23","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":242051,"visible":true,"origin":"","legend":"\u003cp\u003eMap of study area\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7723092/v1/b2abfbf4d2ed956eb5c2ca90.jpeg"},{"id":107350744,"identity":"b95e2c71-c5ba-4ad3-8ec3-d44fe75f1f7c","added_by":"auto","created_at":"2026-04-20 16:02:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1253351,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7723092/v1/7dfba21c-dc1b-4ea7-899e-2d43c042534a.pdf"},{"id":93557102,"identity":"12339d4d-3e56-4feb-b49d-5d79d1e26a59","added_by":"auto","created_at":"2025-10-15 06:48:23","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":165926,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-7723092/v1/a786193a9aa151073d19d191.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the Determinants of Performance in Solid Waste Management: An Analysis of Municipalities in Ghana","fulltext":[{"header":"Highlights","content":"\u003cp\u003eExpenditure per Capita Analysis for Waste Companies Solid Waste Management.\u003c/p\u003e\u003cp\u003eBenchmarking Efficient Waste Companies using Data Envelopment Analysis\u0026middot;\u003c/p\u003e\u003cp\u003eComparing Technical and Scale efficiencies of waste companies in rural/urban settings\u0026middot;\u003c/p\u003e\u003cp\u003eVariable selection for efficiency determination in solid waste management.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eAccording to the World Bank [1], waste generation is projected to drastically outpace population growth by more than double by 2050. The report estimates that 2.01 billion tonnes of municipal solid waste are generated annually, with at least 33% of that not being managed in a way that protects the environment. Improper management of municipal solid waste (MSW) poses severe ramifications for human beings - cholera epidemics [2] and environmental consequences - annual flooding in many parts of the world is a direct consequence [3, 4]. While municipal authorities recognise the need to formulate comprehensive policies to manage and address sanitation needs adequately, concerns exist about the cost implications [5\u0026ndash;7]. More challenging for municipalities and districts in Ghana is not only their inability to formulate solid waste management (SWM) policies but also to develop an objective pathway to evaluate the performance of these policies. Applying subjective measures to determine efficiency often results in disputes and chaos. To ensure standardization of the process, the research was designed the study to:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003eapply the input-oriented data envelopment analysis (DEA) model to measure the efficiency of waste companies in municipal and district areas in Ghana\u003c/li\u003e\n \u003cli\u003eexplain the observed variations in efficiency by testing a set of hypotheses.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eEnsuring efficiency in MSW is more important than ever due to the rising management costs. Karak et al. [8] explain that SWM constitutes the single highest budget item for many local administrations in low-income countries, comprising nearly 20% of municipal budgets, 10% for middle-income countries, and 4 % in high-income countries on average [9]. Other researchers also argue that, besides the cost, limiting the implementation of MSMW plans, there are issues related to the politicisation of sanitation issues and an inadequate expertise in the management process [10\u0026ndash;12]. Waste companies in rural and urban areas face significant challenges at various stages of waste management. Those in the cities struggle the most, given the population increase in municipal centres due to the exodus of people from rural communities. This situation has created a compelling need to develop a mechanism to manage solid waste in Africa [9, 10].\u003c/p\u003e\n\u003cp\u003eThe amount of waste generated worldwide varies disproportionally across different regions. According to Zicha et al. [13], 35% of the world\u0026apos;s waste comes from countries with high Gross National Income, such as Australia, Canada, Denmark, and the United States (USA). In contrast, Sub-Saharan Africa (AFR) has the lowest generation rate, 0.47 kg/capita/day, representing 6% of global waste [9]. Malinauskaite et al. [14] also estimate that, as of 2015, the average amount of municipal solid waste (MSW) generated annually by each of the approximately 512 million inhabitants of the European Union was 477 kg per capita (see Eurostat report, 2017). In contrast, Ghana\u0026apos;s amount is controversial because of the need for more data. While several studies report that SW generation is around 12,710-13,000 tons [15\u0026ndash;18], the World Bank [1] indicates that Ghana generates 0.51kg/capita/day. This figure is higher than the Sub-Saharan Africa average of 0.46kg per capita/day but less than the world average of 0.74 kg per day/ person.\u003c/p\u003e\n\u003cp\u003eDeveloping a policy framework is the first step in the management process [13, 19, 20]. Developing a comprehensive policy framework or plan also requires a complete understanding of waste characterisation [21,22]. Characterisation determines the management strategy and allowable activities in an economy, from industry and agriculture to transportation. Waste audit studies indicate that food and green waste account for 44 per cent of global waste. Dry recyclables (plastic, paper and cardboard, metal, and glass) amount to another 38 per cent of garbage [1]. According to Miezah et al. [23], the composition of SW in Ghana is 61% organic, 14% plastic, 6% inert, 5% miscellaneous, 5% paper, 3% metals, 3% glass, 1% leather and rubber, and 1% textiles.\u003c/p\u003e\n\u003cp\u003eUnlike other developing countries, Ghana has a robust regulatory framework, the revised environmental sanitation policy (ESP) (2010), which governs waste management. The policy assigns full responsibility for waste management to local government authorities (see Adzawla et al. [16]. The focal areas included in the revised ESP (2010) [60] are capacity development, information, education and communication, legislation and regulation, service levels, sustainable financing and cost recovery, research and development, and monitoring and evaluation management activities challenge [15]. While this waste management infrastructure appears solid on paper, authorities struggle to find a mechanism for implementing and evaluating these policies. This situation is unsurprising because, according to Bel et al. [24], municipal solid waste (MSW) collection and disposal are among the most challenging services for local authorities. This complexity often overwhelms city authorities [24\u0026ndash;28].\u003c/p\u003e\n\u003cp\u003eA common strategy adopted by municipalities in Ghana in dealing with SWM is to change the service arrangement management mechanism from the historically dominated public domain to private or from one private vendor to another [29]. However, this strategy has not yielded efficiency because waste collection statistics across the globe show that only high-income countries, particularly those in Europe and North America, are nearing 100% collection. Lower-middle-income countries account for approximately 51 per cent, and 39 per cent of low-income countries for [30]. More worrying is that about 95% of the 39% of solid waste collected in developing countries is not recycled [30].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eApplying data envelopment analysis (DEA) to this study provides objective criteria for evaluating the efficiency of solid waste management across a set of municipalities and districts in Ghana. This approach enables the identification of an efficient waste company at both the municipal and district tiers in SWM, serving as a benchmark for other competing companies. Additionally, performing statistical analysis on contextual variables identified factors explaining variations in performance. Overall, the study framework provides a systematic approach for waste companies to estimate technical and scale efficiency by comparing expenditure per capita to waste collection per capita per day, thereby assessing efficiency. \u0026nbsp; In this study, the terms \u0026quot;performance\u0026quot; and \u0026quot;efficiency\u0026quot; are used interchangeably, although they have distinct meanings. Efficiency in the context of Data Envelopment Analysis (DEA) was defined through the prism of input minimisation [61]. In contrast, the study defined performance as the ability of waste companies to achieve broader organisational goals, specifically delivering equitable economic and social services in local government areas [62].\u003c/p\u003e\n\u003cp\u003eThe research employs archival data from rural and urban districts/municipalities in Ghana plagued by waste management problems. Our study provides an objective pathway for assessing local governments\u0026apos; policies in an environment of unique cultural settings, where finding resources to deal exclusively with solid waste is challenging because of crowded out other social services in health and education, among other areas. This situation is exacerbated by central governments\u0026apos; tendency to devolve waste expenditure responsibilities to municipalities without providing commensurate funding, thereby straining local budgets [10]\u003c/p\u003e\n\u003cp\u003eThe paper is laid out as follows. Section 2 discusses the determinants of performance in solid waste management. Section 3 describes the institutional framework of SWM in Ghana. Sector 4 describes the research design, and Section 5 discusses the research methodology, data, and model selection. Section 6 describes the estimation procedures and the results of the DEA analysis. The presentation of the results of the determinants of performance in Section 7 follows the above section. Finally, section 8 presents the discussion. The article closes with the conclusions, policy implications and areas of further research.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eDeterminants of Performance Measures of Solid Waste Management\u003c/h2\u003e\u003cp\u003eManaging solid waste is a complex task requiring strategies to improve technical efficiency, i.e., achieving a desirable output with the least input costs while addressing health concerns [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. While the Data Envelopment Analysis (DEA) model yields a single efficiency score for evaluating waste companies performance, it falls short in providing deep insights into the observed variations in technical and scale efficiencies between municipalities (technical efficiency: 0.95; scale efficiency: 0.97) and districts (technical efficiency: 0.87; scale efficiency: 0.76). This study collected data on relevant variables informed by a comprehensive literature review, the existing service delivery structure, and the regulatory framework governing solid waste management in Ghana. The aim was to identify explanatory variables that the Data Envelopment Analysis (DEA) could not capture in the first analysis stage to provide further insights into the efficiency drivers.\u003c/p\u003e\u003cp\u003eGhana has a robust institutional framework for solid waste management, which is governed by the Revised Environmental Sanitation Policy, developed in 2010. The policy harmonised waste and sanitation policies that were scattered in piecemeal legislations.\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e The policy has several guidelines for dealing with integrated waste management, such as the National Environmental Quality Guidelines, 1998; Ghana Landfill Guidelines, 2002; Manual for the Preparation of District Waste Management Plans, 2002; Guidelines for Bio-medical Waste, 2000; Guidelines for the Management of Healthcare and Veterinary Waste, 2002; District Level Environmental Sanitation Strategy and Action Plan (DESSAP), and the National Environmental Sanitation Strategy Action Plan (NESSAP) enacted to control waste and sanitation management decisions that were being applied by various national bodies [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe approach that metropolitan, municipal, and district assemblies (MMDAs) adopt for solid waste management varies significantly across different areas. While some entities outsource management to private companies, others utilise house mechanisms. The overarching framework guiding service delivery in waste management is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThere are several layers of SWM in Ghana. The Environmental Protection Agency (EPA) and Ministry of Local Government and Rural Development (MLGRD) are two upper-tier ministries within the waste management architecture that formulate policy and coordinate and monitor the implementation of SWM activities and programs to be consistent with the tenets of Ghana’s Revised Environmental Sanitation Policy 2010 (Ministry of Local Government and Rural Development, 2010). Metropolitans, Municipalities, and District Assemblies (MMDAs) are the local authorities that implement policies through an in-house, private, or joint model.\u003c/p\u003e\u003cp\u003eMarshall and Farahbakhsh’s [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] study applied this regulatory framework to examine performance based on organisational theory. Their study argued that performance in SWM depends on organisational structure, institutional service delivery arrangement, and socio-cultural variables. A substantial body of literature highlights the impact of contextual variables on the efficiency of solid waste management (SWM) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This is exemplified by Struk and Boďa’s [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] study of Czech municipalities, which identified 12 influential factors, including population density, housing spatial arrangements, bin records, and waste separation incentive programs. Based on local dynamics and the existing literature, the study identified five variables, described in further detail in the variable selection section, as relevant predictors. They included demographic characteristics, tasks and organisational structure of each waste company, motivation (reward mechanisms to incentivise employees), socio-cultural behaviour of the people in the area, and the politicisation of SWM policies. The ISWM assumes that citizens are not mere consumers, but rather that public officials/ bureaucrats design and provide services to satisfy [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e–\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The framework views citizenry as an essential component in formulating and implementing SWM policy, without whom the policy will fail. This concept aligns with the objective of examining the determinants of performance from a multivariate approach: technical, social, cultural, and institutional contexts [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Thus, our hypothesis that:\u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eH\u003csub\u003e1\u003c/sub\u003e: Municipalities where citizens adopt behavior tendencies consistent with existing waste policies will be efficient in managing solid waste.\u003c/p\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe technical perspective argues that most public policies on solid waste management fail at the implementation stage because the personnel charged with SWM implementation responsibilities have low educational levels and limited specialised training, particularly in developing countries [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Due to their low academic qualifications, salaries are also low, which affects their motivation and commitment to work. The study further argues that the new waste management trend requires in-depth knowledge of pluralistic policy and an understanding of the role of multiple interdependent actors in public management [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Inadequate skills or a lack of personnel in SWM will create a gap between policy and implementation. Thus,\u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eH\u003csub\u003e2\u003c/sub\u003e: Employing workers with higher education levels and skills will positively impact solid waste management.\u003c/p\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAdzawla et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] employ a behavioural perspective to explain that the problem facing developing countries, particularly local government authorities in Ghana, in disposing of solid waste is attitudinally or demographically related rather than skill- or income-related. Therefore, institutional measures designed to create acceptable behaviour will reduce the complexity of the social disorder. Given this rationale, our study examined the performance of waste utility companies in an environment of behaviour control measures such as government regulations, taxes, and the encouragement of new technologies in SWM. Regulation determines waste characterisation, management techniques, and allowable activities ranging from agriculture to industry and transportation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The study, therefore, argues that institutional measures, ranging from a code of ethics and sanitation laws to offering something of material, social, or normative value to sanitation employees and the citizenry, prompt them to act as co-producers in SWM [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eH\u003csub\u003e3\u003c/sub\u003e: Municipalities that have instituted sanitation bylaws and regularly update their solid waste management plans will be efficient.\u003c/p\u003e\u003cp\u003eH\u003csub\u003e4\u003c/sub\u003e: Municipalities with reward systems to motivate employees and encourage good citizen behaviour will be efficient.\u003c/p\u003e\u003c/div\u003e"},{"header":"Research Design and Methods","content":"\u003cp\u003eThis study uses a cross-sectional design to assess the efficiency of the SWM of municipalities in Ghana for the fiscal year 2022–2022. The study covered five geographical regions\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The locations selected in districts and municipalities in Ghana faced issues of increasing urbanisation and high levels of economic activities accompanied by high solid waste generation. These issues impose significant difficulties for SWM companies. The research methodology employed in this study consisted of a two-stage approach. The first stage involved the application of the data envelopment analysis (DEA) to conduct an efficiency analysis, with the primary objective of assessing the performance of waste management companies operating within municipal/district jurisdictions. The research computed efficiency using the ratio of the number of skip containers and compactors, cost data of inputs - operational cost (OPEX)- current expenditure, and cost of labour, gathered from municipalities' budgets, quarterly reports, and annual audited accounts for the 2021–2022 fiscal year, to the yearly tons of solid waste, which was estimated using the number and size of utilities' skip, compact, and roller containers [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the second stage, a robust multiple linear regression analysis was conducted to ascertain how the data confirms the theory. The independent variables selected for the analysis consisted of organisational structure, institutional arrangements, social and cultural context, and the dependent variable was the efficiency scores obtained in the first stage. These study variables were based on the research context and the literature on SWM, which is described in detail in the variable selection section [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/p\u003e\u003ch3\u003eApplication of the DEA Technique\u003c/h3\u003e\u003cp\u003eOur study applied IBM's statistical software package \"Stata17\\StataSE-64.exe\" to run the results for practical purposes. A mathematical procedure described section in Appendix A section shows the theory and the computational script for evaluating the relative efficiency of waste utilities. The tool is configured to run the results using the input orientation DEA Model by default. Also, preceding running the DEA results is the data processing stage, which essentially refers to configuring the raw data (inputs and outputs) from the field into a format compatible with the software. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the raw field data preprocessed for the DEA analysis.\u003c/p\u003e\u003ch3\u003eDEA Inputs and outputs Selection\u003c/h3\u003e\u003cp\u003eThis study's inputs consisted of Operational expenditure OPEX (Salary + Fuel + Electricity + Repairs), cost of labour, and the number of skip containers and compactors gathered from the budgets of districts and municipalities for the 2020–2021 fiscal year. There was, however, no data on the amount of solid waste generated, as is the case for most local governments [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Therefore, data on the annual tons of rubbish each municipality generates were obtained transitively using the size of the containers and the weekly collection cycle. To Illustrate:\u003c/p\u003e\u003cp\u003e1. Skip = 220 metric tons, 1 Roll-on = 250 metric tons, 1 compactor = 250 metric tonnes. The total metric of rubbish produced for the fiscal year 2020–2021 was estimated based on each municipal truck's weekly trips to landfill sites. A summary of the data is shown in the Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003ch3\u003eRationale for Input-Oriented DEA Theoretical Model\u003c/h3\u003e\u003cp\u003eThe rationale for applying the input-oriented DEA model which is theoretically grounded in the literature Rogge and De Jaeger [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]; Pérez-López et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]; Expósito and Velasco [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]; Struk and Boďa [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]) to estimate the efficiencies and rank waste companies lies in the study's objective of input minimization- seeking to achieve the maximum proportional reduction in input usage that is compatible with the technology set [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. By applying this technique, enabled us to establish an efficiency frontier of high-performing companies to serve as benchmarks to measure the relative efficiency of average and non-performing companies.\u003c/p\u003e\u003cp\u003eConsistent with the existing literature [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], the input-oriented DEA model proved effective in comparing the performance of waste companies in Ghana, particularly in an operational environment characterized by limited information on input and output prices [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This efficiency approach promotes economic rationality, which can help Ghanaian municipalities optimize resource allocation and reduce unnecessary expenditures.\u003c/p\u003e"},{"header":"Results and Analysis","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below presents the input data, consisting of operational expenditure (OPEX), the number and size of skips, rollers, and compactors used to generate the annual tons of solid waste collected by each municipality. By applying IBM's statistical software package, Stata 17 (StataSE-64.exe), our study conducted the input-oriented model DEA model to determine the efficiency score, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, i.e., a comprehensive overview of the relative performance of each waste company, from technical to scale efficiencies, as well as returns to scale, which indicate whether utilities are operating under increasing or decreasing returns to scale. To ensure a practical comparative analysis, waste companies were grouped by district and municipal tier levels to compare technical and scale efficiencies, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. A score of one demonstrates the highest efficiency on an efficiency scale of 0–1. The analysis also focused on the expenditure per capita that each company incurs to collect waste and the daily kilogram of waste collected, providing more insight into the cost efficiency analysis, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The waste estimation model outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e offers a reliable framework for local authorities to quantify waste generation in municipalities and districts thereby overcoming the limitation in the non-available of data. By providing accurate estimates of daily waste production, this model can inform effective waste management planning and policy-making. The ability to estimate the amount of waste generated daily enabled us to determine the expenditure per capita and the amount of waste produced in kilograms per capita per day, as described earlier in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Using this estimation approach, the study was able to make statistical inferences on whether a waste company was utilising more or fewer resources in the collection of solid waste, as shown in Fig. A2 of Appendix A.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\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\u003eSolid waste management data for municipalities districts in Ghana for the 2021–2022 period\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMunicipality/\u003c/p\u003e\u003cp\u003eDistrict Capitals\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of Employees\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of skip Containers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of Compactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOperational\u003c/p\u003e\u003cp\u003eExpenditure\u003c/p\u003e\u003cp\u003e(OPEX)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYearly Estimate in metric tons\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGONA EAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e256690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17,600\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGONA WEST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e693800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22,880\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASSIN SOUTH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e326900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e24,640\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASSIN NORTH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e406800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19,360\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAJU_ ENY_ ESS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e425300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15,840\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASIK_ODOB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e598000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e21,120\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIBIANI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e294060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e40,600\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWIAWSO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e367850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11,560\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJUABOSO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e298470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14,080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAKONTOMBRA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1202500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10,560\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBODI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e276690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7,040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIA EAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e526500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14,080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIA WEST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e499200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10,560\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUAMAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e419400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12,320\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAOWIN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e345500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19,360\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMENFI WEST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e410000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e21,120\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMENFI CENTRAL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e228000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14,080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eATW_ MPO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e387000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19,360\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEJISU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e366758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10,910\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASANTE BEKWAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e184756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30,920\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASE_ MAN_ AKR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e141924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2,420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGOASO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e301000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23,880\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAHANTA WEST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e230690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12,440\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTARKWA MUNI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e602231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e37,960\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUPP_ DEN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e179000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12,320\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWASSA EAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e269760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e24,200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows annual data for inputs, OPEX, labour, number of solid waste collection objects (compactors, skip containers) used to estimate annual tons of solid waste in each municipality.\u003c/p\u003e\u003cp\u003eData collected manually from archival records of MMDAs for the year ending 2022.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\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\u003eEfficiency values for Waste Companies on VRS and CRS\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMUNICIPALITIES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCRS_TE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVRS_TE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNIRS_TE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSCALE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRTS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGONA_EAST_\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.500189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.726424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.590503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.688564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGONA_WEST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.650246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.745468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.739140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.872266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASSIN_SOUTH_\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.650246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.747278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.674105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.870153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASSIN_NORTH_\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.650246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.800932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.679612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.811862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAJU__ENY__ESS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.650246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.859095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.687556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.756897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASIK_ODOB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.650246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.763648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.753602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.851500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIBIANI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWIAWSO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.310911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.498985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.325325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.623087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJUABOSO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.650246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.920265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.687184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.706586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAKONTOMBRA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.650246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.650246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBODI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.549097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.708374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.624049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.775151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIA_EAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.658916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.901201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.731153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIA_WEST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.650246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.650246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUAMAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.650246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.951757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.823431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.683206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAOWIN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.862867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.952597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.986922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.905804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMENFI_WEST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.600227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.720446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.648121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.833133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMENFI_CENTRAL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.650246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.932178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.678954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.697556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eATW__MPO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.650246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.803030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.809741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEJISU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.319104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.499097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.336673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.639363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASANTE_BEKWAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASE__MAN__AKR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.943760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.943760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGOASO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.931281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.959276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.970816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAHANTA_WEST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.485140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.736416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.486655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.658785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTARKWA_MUNI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.667840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.676622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.987021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUPP__DEN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.739169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.778339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.739169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWASSA_EAST_\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.820661\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.938286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.847917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.874639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows Scale Efficiency(SE), Return to Scale (RTS), ranking of Technical efficiencies (TE) for Waste Companies on CRS and VRS. VRS Frontier (-1: drs, 0: crs, 1: irs)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003eEfficiency comparison for waste companies in districts/municipalities\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistricts Category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTechnical Efficiency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScale Efficiency\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGONA_EAST_AGONA_AST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.726424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.688564\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASSIN_SOUTH_\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.747278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.870153\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAJU__ENY__ESS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.859095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.756897\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASIK_ODOB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.763648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.851500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJUABOSO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.920265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.706586\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAKONTOMBRA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.650246\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBODI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.708374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.775151\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBia East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.901201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.731153\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIA_WEST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.650246\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUAMAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.951757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.683206\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAOWIN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.952597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.905804\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMENFI_CENTRAL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.932178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.697556\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eATW__MPO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.803030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.809741\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASE__MAN__AKR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.943760\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAHANTA_WEST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.736416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.658785\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUPP__DEN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.739169\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWASSA_EAST_\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.936267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.874639\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.878737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.764303\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMunicipal Category\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTechnical Efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScale Efficiency\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGONA_WEST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.745468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.872266\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASSIN_NORTH_\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.800932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.811862\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIBIANI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWIAWSO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.49898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.623087\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMENFI_WEST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.720446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.833133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEJISU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.499097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.639363\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASANTE_BEKWAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGOASO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.959276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.970816\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTARKWA_MUNI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.676622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.987021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUPP__DEN_East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.739167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.790082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.847394\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eA more detailed graphical representation is also shown in Fig. A1 of Appendix A.\u003c/p\u003e\u003ch3\u003eVariable Selection to Explain Variations in the Efficiency of Waste Management Companies\u003c/h3\u003e\u003cp\u003eFollowing the efficiency assessment, an investigative process was conducted to determine the factors underlying the observed variations in technical and scale efficiencies among the waste companies. This systematic and comprehensive literature review was performed to identify indicators impacting performance using Web of Science (webofscience.com), Google Scholar (scholar.google.com), and other databases. This search technique applied several variant keywords relating to 'performance', 'efficiency', and ‘solid waste’, yielding a large pool of articles from the literature.\u003c/p\u003e\u003cp\u003eThe identified indicators from these papers were subsequently organised under thematic categories, encompassing the legal and institutional framework, cultural contex, social context, and technical \u0026amp; organizational structure, and institutional arrangement health pertinent to SWM in Ghana. The study applied this categorisation given the unique environment for solid waste management, as determined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The criteria for selecting and excluding performance indicators was based on Trawick’s [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] framework for selecting indicators: relevance to scope, measurability, performance-based, scientific robustness, comparability, data quality, data availability, broad acceptance, compliance, consensus-based, clarity, and transparency.\u003c/p\u003e\u003cp\u003eThe third stage after selecting the predictors involved gathering data related to these variables through qualitative surveys. The final stage involved performing a robust multiple linear regression to check whether the data confirms the theory. The categorical variables selected from this process consisted of technical -academic qualification and training, institutional arrangements, social, and cultural context. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the variable descriptions, measurement items, and scales of measurement. These variables were grouped as independent variables impacting performance (PQ), whose value was obtained from the DEA analysis in the first stage.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003eDescription of performance determinants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTechnical \u0026amp; organizational structure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInstitutional Arrangement (IA)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSocial context) (SC)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCultural Context (CC)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTechnical or job competencies acquired through education [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDesign and Implementation of regulatory regime [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRewards for regulatory compliance. Verschuere et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], Normann [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDescribes the attitudinal or demographic text affecting waste management Adzawla et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItems of measurement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducation level, specialized training, salary appreciation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEthics and code of conduct, sanitation bylaws\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFees, taxes, punishments, incentives, etc.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eburning of waste, littering around, waste containers management, etc.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe items were rated on a five-point Likert scale: 1, strongly disagree; 2, disagree; 3, Prefer not to answer; 4, agree; 5, strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe items were rated on a five-point Likert scale: 1, strongly disagree; 2, disagree; 3, Prefer not to answer; 4, agree; 5, strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe items were rated on a five-point Likert scale: 1, strongly disagree; 2, disagree; 3, Prefer not to answer; 4, agree; 5, strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eThe items were rated on a five-point Likert scale: 1, strongly disagree; 2, disagree; 3, Prefer not to answer; 4, agree; 5, strongly agree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\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\u003eMunicipalities/districts Operational expenditure, expenditure/capita \u0026amp; waste/kg/day\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMunicipalities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExpenditure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExpenditure\u003c/p\u003e\u003cp\u003e/capita\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYearly Tons\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e*10\u003csup\u003e6\u003c/sup\u003eKg\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eKg/Capita/day\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIBIANI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e167,971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e294,060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40,600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSEFWI WIAWSO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e151,220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e367,850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11,560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGONA WEST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136,882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e693,800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22,880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASSIN NORTH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e161,341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e406,800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19,360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGOASO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e150,198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e301,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23,880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMENFI_WEST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e129,882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e410,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21,120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEJISU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e180,723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e366758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10,910\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASANTE BEKWAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137,967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e184,756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30,920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTARKWA NSUAYEM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e218,664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e602,231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37,960\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e159,428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e403,028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24,354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24.354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistricts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRec/Exp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExp/Capita\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e*10\u003csup\u003e6\u003c/sup\u003eKg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eKg/Capita/day\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGONA EAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e256690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17,600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASSIN SOUTH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e326900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22,880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAJU_ENY_ESS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e425300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15,840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASIKUMA_ODOBEN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e126993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e598000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21,120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJUABOSO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e298470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14,080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAKONTOMBRA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1202500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10,560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUPPER_DEN_WEST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e179000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12,320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIA_EAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e526500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14,080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIA_WEST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e499200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10,560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUAMAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e419400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12,320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eATWIMA_MPONUA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e155254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e387000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19,360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAHANTA_WEST_\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e153140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e230690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12,440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBODI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e276690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7,040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMENFI CENTRAL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e228000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14,080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASENE MANSO AKROSO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e141924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2,420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWASSA EAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e269760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24,200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAOWIN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e129721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e345500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19,360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101439.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e388913\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14,721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the expenditure the per capita and the amount of waste collected by companies in municipalities and districts. The results of the descriptive statistics, multivariariate and the correlation analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\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\u003eDescriptive Statistics and Multivariate Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVARIABLES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003esd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerformance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnical \u0026amp; Organizion Structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstitutional arrangement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial context\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCultural context\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMODEL RESULTS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVARIABLES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganizational structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.015*\u003c/p\u003e\u003cp\u003e(0.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstitutional Arrangements\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003cp\u003e(0.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Context\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.004**\u003c/p\u003e\u003cp\u003e(0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCultural context\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.93e-08 ***\u003c/p\u003e\u003cp\u003e(0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.97e-06 ***\u003c/p\u003e\u003cp\u003e(0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\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\u003eCorrelation coefficients between variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePerformance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTechnical \u0026amp; organizational structure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInstitutional Arrangement (IA)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSocial context (SC)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCultural Context (CC)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnical \u0026amp; organizational structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnical \u0026amp; organizational structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstitutional Arrangement (IA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCultural Context (CC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eThe correlation is significant at *0.005\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis two-pronged discussion is grounded in the findings of the Data Envelopment Analysis (DEA) conducted in Stage 1 to determine the efficiencies of waste companies and the robust multiple linear regression on contextual variables aimed at elucidating the factors underlying the observed variations in efficiency. The results show that the technical efficiency for waste companies in the districts was 0.87 compared to 0.79 for those in the municipalities. If technical efficiency θ* \u0026lt; 1, then 1\u0026thinsp;\u0026minus;\u0026thinsp;θ* represents the maximum radial reduction of the inputs, which will still ensure that the given outputs are producible for a waste company [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The objective function of this model is to find a minimal value for an 'intensity' factor \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\theta\\:\\)\u003c/span\u003e\u003c/span\u003e which corresponds to the waste utility\u0026rsquo;s ability, or the potential for a proportional reduction in all the inputs [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Except for Akontombra, Asene Manso Akroso, Bia West, Upper Denkyira West and East, and Bibiani, and Asante Bekwai, all the waste companies failed to achieve θ* = 1, indicating the presence of slacks in their operations, as shown in Fig. A2 in the Appendix A.\u003c/p\u003e\u003cp\u003eAlso, the study found that waste companies in municipalities had a scaling efficiency of 0.85 compared to 0.76 for districts. This indicates that municipal waste companies could focus on scale efficiencies while those in smaller communities\u0026rsquo; work on improving technical efficiencies. This result also demonstrates that scale efficiency tends increase as the size of infrastructure increase. There is an unsettled view in the literature regarding the relationship between a company\u0026rsquo;s size and its efficiency. While the inverse relationship between scale efficiency and municipal infrastructure size is a well-established finding in the literature (Brettenny \u0026amp; Sharp [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], other studies show conflicting findings. For instance, Guerrini et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] suggest a positive association between technical efficiency and size, whereas Berg and Marques [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] reveal a negative relationship.\u003c/p\u003e\u003cp\u003eAlthough the average technical efficiency for waste companies in municipal and district areas is better than generally believed, an individual examination shows that many are cost-inefficient and operate below the 75% quartile in solid waste collection. Notably, Ejisu, Sefwi Wiawso, and Tarkwa Nsuayem as indicated in Fig. A2 in the Appendix A. This pattern is consistent with the study of Rogge and De Jaeger [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] for municipalities in Flanders, Belgium. Further analysis, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates that municipalities produced 0.264kg/capita/day more solid waste than districts for the fiscal year 2021\u0026ndash;2022. In other words, waste collection for companies in the municipalities was 0.42 Kg/Capita/day compared to 0.156Kg/Capita/day for companies in the districts. Although 0.42 Kg/Capita/day is far higher compared to the districts, it is lower when compared to the average Ghana value of 0.51kg/capita/day and the Sub-Saharan regional average of 0.46kg/capita/day [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The relatively small average value for the district does not also suggest an improvement in the sanitation situation, as most of the waste in the rural areas is openly dumped and burned and, therefore, does not even find its way into the waste bins for collection [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, a comparative analysis of expenditure per capita for waste collection revealed that municipal companies incurred a lower cost of 2.53, whereas rural companies expended 3.833. Conversely, municipal companies collected a higher amount of waste, 0.42kg/capita/day, compared to the 0.156kg/capita/day collected by their rural counterparts. This trend suggests a significant waste collection efficiency and expenditure disparity between these two contexts.\u003c/p\u003e\u003cp\u003eWhile statistical inference from the DEA analysis results [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] can be used to determine the companies' inputs and outputs, the technique does not provide insight into the underlying factors driving the variations in performance among the waste companies, as observed in the first stage of the DEA analysis. To explain the observed variations in efficiency among the waste companies, categorical variables were identified using Trawick\u0026rsquo;s [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] framework\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e. The framework allowed us to select and group variables that can impact performance based on a literature search, the legal framework for waste delivery in Ghana, and other contextual factors. In the initial stages of our analysis, we checked the data's normality using the Shapiro-Wilk test [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] to inform the choice of the statistical test. The test revealed that the data was not normally distributed and that outliers were confirmed by the negatively skewed value of -0.4575038.\u003c/p\u003e\u003cp\u003eOur study, therefore, utilised the robust multiple linear regression (RMLR) analysis method due to the limited sample size, where outlier detection is essential to ensure the accuracy and reliability of the results. The robust multiple linear regression enabled us to predict the collective effect of all the variables and determine the impact of each of the variables on performance. Overall, the variables showed varying correlations among themselves. For instance, as shown by Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, there were strong positive correlations between Institutional arrangement and cultural context (0.80). Social context and institutional arrangements showed a moderately strong correlation (0.63) compared to (0.51) for Task and organisational structure and culture. Social context and Organisational structure showed the weakest correlations of 0.12, suggesting they were not asynchronous. A computed standard Cronbach's alpha of 0.82 also shows a reasonable internal consistency of the scale used to measure the items. Again, it also indicates that the items share a substantial degree of covariance, reflecting a reasonable level of reliability in measuring the underlying construct.\u003c/p\u003e\u003cp\u003eThe analysis of the robust multiple linear regression revealed that while some variables had a positive and significant impact on performance, others showed a positive but insignificant impact. For instance, the results indicate that performance is positively related to the social arrangements that waste companies adopt in their waste management services (t\u0026thinsp;=\u0026thinsp;3.193, p\u0026thinsp;=\u0026thinsp;0.004). The implication is that rewards and offering material incentives can impact people's behaviour in a community, which can significantly impact waste management performance. The conclusion is that waste companies operating in municipalities and districts where citizens tend to adopt behaviour patterns consistent with existing waste policies are more efficient in managing solid waste, as supported by the literature [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. However, the results contradict previous studies on Ghana by Agbefe et al. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and Adzawla et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], which attribute low performance in SWM to urbanisation and poor collection regimes. Again, the results show that the impact of institutional measures on performance was not significant, indicating that waste companies lack an incentive structure within their operational policies to regulate and encourage hard work and personal agency. On the one hand, the results also show that performance depends on the nature of sanitation employees' tasks, given their related educational qualifications. (t\u0026thinsp;=\u0026thinsp;2.62, p\u0026thinsp;=\u0026thinsp;0.015).\u003c/p\u003e\u003cp\u003eGiven the significant impact of Technical skill upgrade on performance in an organisation with a well-defined structure, it follows that employing workers with higher education levels and technical skills will positively impact solid waste management. This result is consistent with the study of Buenrostro and Bocco [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], which established a positive relationship between higher educational performance. Our study results also show that cultural variables, such as people's behaviour, significantly impact a municipality's performance in SWM. Although the results demonstrate that the amount of waste generated and collected by companies in the cities far exceeds districts, considering that city areas have issues with higher population densities [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], the latter was more cost-efficient, with average expenditure per capita for solid waste being 3.8GHc compared to 2.5GHc for utilities in the cities.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe two-stage research framework employed in this study provides a systematic and objective approach for city authories to analyse waste companies' performance and estimating the amount of waste that local governments generate. Furthermore, this framework sheds light on the causes of variations observed in their performance. By utilising this framework, researchers and policymakers can gain valuable insights into the complexities of waste management and develop more effective strategies for improvement. The waste estimation approach should solve the problem of the lack of data on the amount of waste generated in rural and urban areas, which arguably affects policy planning and implementation [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe study shows that, on average, waste companies in municipalities were technically more efficient (0.87) compared to those in districts (0.79). Optimising efficiency requires that waste companies in the municipalities to radially reduce inputs 1- θ or by 13% whereas companies in the districts need to reduce their inputs by 21% to be efficient [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The study found that while the expenditure per capita for waste collected by municipalities (3.45) was lower than that for utilities in the districts (3.833), the amount of waste collected was 0.156kg/capita/day, compared to 0.42kg/capita/day for companies. The expenditure per capita analysis can provide city authorities with the information needed to identify slack variables and make informed changes. The measurement of the scale efficiency gave the study an idea of the source of inefficiency, i.e., increasing or decreasing returns to scale. For example, the scale efficiency results showed that except for the waste company in Asante Bekwai, whose source of scale efficiency was due to decreasing returns to scale- production of an inefficiently large amount of output, most companies in districts and municipalities showed increasing returns to scale \u0026ndash; applying massive inputs to produce an inefficiently small amount of output.\u003c/p\u003e\u003cp\u003eOur study presents a systematic approach for selecting variables that positively or negatively impact waste management performance. This framework enables managers to identify contextual variables that impact the provision of waste management services in local areas, or adjust policies, particularly when dealing with variables outside their control. Overall, the results demonstrate that social context has a significant impact on performance, confirming the theory that performance in waste management is determined by the social structure, such as sanitation bylaws and motivation to encourage good citizen behaviour [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. However, the impact of institutional arrangements further demonstrates that waste companies' operations in local government areas lacked institutional measures to motivate and regulate sanitation workers, thereby showing an insignificant impact on performance. Additionally, the results show that the tasks employees perform, given their related educational qualifications, significantly impact performance [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Furthermore, the results revealed cultural aspects, i.e., people's attitudes and behaviours [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], as significant performance determinants, as confirmed by the theory of performance and cultural antecedents [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and future research\u003c/h2\u003e\u003cp\u003eOne constraint of this study, similar to numerous others, is the accessibility and reliability of the data. Despite Ghana enacting the Right to Information Act, seeking institutional data for academic purposes was seen as a potential effort to uncover misconduct. The research tackled this challenge by utilizing excerpts from secondary sources, including independent entities such as parliament and the audit service. Our research concentrated on annual data, which restricts our ability to observe efficiency trends over time. The research suggests utilizing the Malmquist productivity index [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] and additional methods like the bootstrap [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] in conjunction with the DEA technique to assess efficiency across several fiscal years. By implementing these strategies, future studies can expand on our results and offer a deeper comprehension of efficiency within the solid waste management to provide a more nuanced understanding of efficiency in the SMW. Another area of future research is the application of the study methods to examine the inconsistency between the impact of institutional arrangements and SWM using data from other communities.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was made possible through the technical advice and supervisory direction of\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProf Ant\u0026oacute;nio Fernando Tavares Associate Professor w/ Habilitation in Political Science School of Economics and Management University of Minho 4710-057 Braga, Portugal. Webpage: https://sites.google.com/site/umpianonafloresta/home; URL: https://www.eeg.uminho.pt/en/_layouts/15/UMinho.PortaisUOEI.UI/Pages/userinfo.aspx?p=1925.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e:\u0026nbsp;No applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e:\u0026nbsp;The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u003c/strong\u003e All data generated or analysed during this study are included in this published article [and its supplementary information files]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Moses Kofi Armah and Miguel \u0026Acirc;ngelo Vilela Rodrigues. The first draft of the manuscript was written by Moses Kofi Armah and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e:\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Bank (2018) What a Waste 2.0: A global snapshot of solid waste management to 2050 (urban development series). https://datatopics.worldbank.org/what-a-waste/\u003c/li\u003e\n\u003cli\u003eAgbefe LE, Lawson ET, Yirenya-Tawiah D (2019\u003cem\u003e) \u003c/em\u003eAwareness on waste segregation at source and willingness to pay for collection service in selected markets in Ga West Municipality, Accra, Ghana\u003cem\u003e. \u003c/em\u003eJ Mater Cycles Waste Manag\u003cem\u003e \u003c/em\u003e21\u003cem\u003e(\u003c/em\u003e4\u003cem\u003e):905\u0026ndash;914\u003c/em\u003e. https://doi.org/10.1007/s10163-019-00849-x\u003c/li\u003e\n\u003cli\u003eSharholy M, Ahmad K, Mahmood G, Trivedi RC\u003cem\u003e \u003c/em\u003e(2008)\u003cem\u003e \u003c/em\u003eMunicipal solid waste management in Indian cities\u0026ndash;A review\u003cem\u003e. \u003c/em\u003eWaste Manag\u003cem\u003e \u003c/em\u003e28\u003cem\u003e(\u003c/em\u003e2\u003cem\u003e):459\u0026ndash;467. \u003c/em\u003ehttps://doi.org/10.1016/j.wasman.2007.02.008\u003c/li\u003e\n\u003cli\u003eRathi S (2006) Alternative approaches for better municipal solid waste management in Mumbai, India. 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Journal of Applied 930 Economics, 8(1), 171-190. https://doi.org/10.1080/15140326.2005.12040623\u003c/li\u003e\n\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":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Solid Waste Management, Data Envelopment Analysis, Local Government, Organisation Theory","lastPublishedDoi":"10.21203/rs.3.rs-7723092/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7723092/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study applied the input-oriented data envelopment analysis technique to assess the performance of solid waste management companies operating in Ghana's municipal and district assemblies (MDAs). The results showed that the average technical efficiency for waste companies in municipalities was 0.79, compared to 0.86 for those in districts. Further analysis revealed that, despite districts spending more on solid waste management per capita (3.833) compared to municipalities (2.53), their waste collection rates were significantly lower, at 0.156 kg/capita/day, compared to 0.42 kg/capita/day in municipalities. Nonetheless, both districts and municipalities fell short of the national average waste collection rate of 0.51 kg/capita/day. A robust multiple linear regression analysis identified organisational structure, cultural and social context as significant determinants of performance. The application of the input-oriented data envelopment analysis technique provides a framework for assessing, monitoring, and benchmarking the performance of waste management companies.\u003c/p\u003e","manuscriptTitle":"Assessing the Determinants of Performance in Solid Waste Management: An Analysis of Municipalities in Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 06:48:18","doi":"10.21203/rs.3.rs-7723092/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-01T07:08:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-29T19:21:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254608355097462668307769244140481636967","date":"2025-11-24T11:22:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-21T07:24:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253753113079022351884247606310103957123","date":"2025-11-03T12:28:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279467040451646806830436009059638431872","date":"2025-11-01T21:07:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159103688234704236333151852394272458896","date":"2025-10-21T18:44:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-01T13:42:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-29T16:56:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-27T14:55:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-27T14:55:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-09-26T15:03:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc0478a7-e324-40c3-8be5-26edb8008f4f","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:00:56+00:00","versionOfRecord":{"articleIdentity":"rs-7723092","link":"https://doi.org/10.1007/s43621-026-02793-x","journal":{"identity":"discover-sustainability","isVorOnly":false,"title":"Discover Sustainability"},"publishedOn":"2026-04-16 15:57:49","publishedOnDateReadable":"April 16th, 2026"},"versionCreatedAt":"2025-10-15 06:48:18","video":"","vorDoi":"10.1007/s43621-026-02793-x","vorDoiUrl":"https://doi.org/10.1007/s43621-026-02793-x","workflowStages":[]},"version":"v1","identity":"rs-7723092","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7723092","identity":"rs-7723092","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}