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The comparative analysis of the effect of economic policy uncertainty on environmental sustainability is imperious as it can provide critical insights into the link between economic policies and environmental sustainability. Economic policy uncertainty may have different impacts in different economies. The present study provides a comparative analysis of the effect of economic policy uncertainty on environmental sustainability in developed and emerging economies. The study employs pooled ordinary least squares and panel quantile regression to analyze data from 2001 to 2019. Moreover, the study also compares the impact of economic policy uncertainty on environmental sustainability across two different econometric methods. It also compares the results across different quantiles of the distribution of variables. Moreover, the study includes the agriculture output, renewable energy consumption, and foreign direct investment in the model. The results show that economic policy uncertainty negatively and significantly impacts environmental sustainability as it increases GHG emissions. Moreover, agriculture output increases GHG emissions in developed economies at higher quantiles. Furthermore, the results also confirm the pollution haven hypothesis, while renewable energy consumption has a positive effect on environmental sustainability as it significantly reduces GHG emissions. The study stresses that governments should take measures to minimize economic policy uncertainty to improve environmental sustainability. In addition, effective policies to enhance openness in the policymaking process and offer long-term policy certainty and foster more stable investment conditions would encourage renewable energy and reduce GHG emissions.

Sustainable agriculture is crucial to the United Nations’ efforts to promote the Sustainable Development Goals (SDGs). However, to develop successful policies and strategies, it is necessary to assess the many obstacles to implementing sustainable agriculture. This study uses Multi-Criteria Decision Making (MCDM) techniques to analyze the challenges and opportunities facing sustainable agriculture in China’s economy, particularly in advancing the SDGs. Three enormous obstacles are found in the study, along with fifteen smaller ones that are broken down into economic, social, and environmental categories. The weights of the obstacles and sub-barriers are determined, and the solutions for sustainable agriculture are ranked using the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methodologies. According to the AHP approach, economic issues are China’s most significant obstacles to sustainable agriculture, followed by environmental and social concerns. Climate change and a lack of financial incentives are the two highest-ranked sub-barriers. On the other hand, the SAW approach suggests that the best ways to achieve the SDGs through sustainable agriculture are through international cooperation, financial investments in sustainable agriculture, and alternative agricultural methods. In order to advance sustainable agriculture and the SDGs in China, the report advises policymakers to focus on strengthening institutional support, increasing public awareness, and making technological investments.

A significant effort is required to reduce China’s dependency on fossil fuels while also supporting worldwide efforts to reduce climate change and develop hydrogen energy systems. A hydrogen economy must include renewable energy sources (RESs), which can offer a clean and sustainable energy source for producing hydrogen. This study uses an integrated fuzzy AHP–fuzzy TOPSIS method to evaluate and rank renewable energy sources for developing a hydrogen economy in China. This is a novel approach because it can capture the uncertainty and vagueness in the decision-making process and provide a comprehensive and robust evaluation of the alternatives. Moreover, it considers multiple criteria and sub-criteria that reflect the environmental, economic, technical, social, and political aspects of RESs from the perspective of a hydrogen economy. This study identified five major criteria, fifteen sub-criteria, and six RES alternatives for hydrogen production. This integrated approach uses fuzzy AHP to evaluate and rank the criteria and sub-criteria and fuzzy TOPSIS to identify the most suitable and feasible RES. The results show that environmental, economic, and technical criteria are the most important criteria. Solar, wind, and hydropower are the top three RES alternatives that are most suitable and feasible. Furthermore, biomass, geothermal, and tidal energy were ranked lower, which might be due to the limitations and challenges in their adoption and performance in the context of the criteria and sub-criteria used for the analysis. This study’s findings add to the literature on guidelines to strategize for renewable energy adoption for the hydrogen economy in China.

In the wake of the Paris agreement, adaptation and mitigation efforts are underway to combat climate change. Despite the promulgation of environmental policies, rules and regulations, and the setting of local environmental regulations, the objectives of cleaner industrial production, and sustainable environmental management are still far from the targets set. However, the stringent implementation of environmental policy is equally important as the decree of environmental laws. Environmental conflicts between the government and industry may source from lenient or less strict policy implementation and/or weak administrative capacity of the governments’ agencies. The use of the government's inherent power can play a pivotal role in determining the preferences of the industry regarding environmental sustainability. This study proposes a power-asymmetric based negotiation strategy by incorporating formal mathematical formulations of the power-asymmetric preferences and stability concepts in the Graph Model for Conflict Resolution (GMCR). A case study of government-industry conflict is resolved to confirm the procedure and application of the proposed methodology. The analysis shows how the decision-maker – industry adjusts its preference when the government imposes strict environmental policy. It provides valuable strategic insights that use of government's inherent power in the stringent implementation of environmental policies is indispensable for cleaner industrial production and sustainable environmental management.

Green finance (GF) is a vital strategy implemented by China to minimize carbon emissions to achieve targets of carbon peak and carbon neutrality. Thus, the objective of this study is to reduce carbon emissions by developing green finance practices in China. This study identifies, evaluates, and ranks the factors and sub-factors of green finance because it is the core issue for sustainable development. In this regard, this study utilizes the Delphi and fuzzy Analytical Hierarchy Process (FAHP) method to analyze the main factors and sub-factors of green finance. The Delphi method finalizes the 6 factors and 26 sub-factors after a thorough investigation. The FAHP method was used to assess and rank the identified factors and sub-factors of green finance. The findings show that the political (POF) is the most crucial factor of green finance in the Chinese economy. The economic (ECF) and environmental (ENF) factors are ranked second and third important factors. The further results of the FAHP reveal that ecological and political identification (POF1), political stability (POF3), and climate commitments (POF2) are the top-ranked sub-factors of green finance. The results specify that green finance development is a very crucial strategy to minimize carbon emissions.

The growing environmental, economic, and social problems have forced governments and policymakers to ensure a sustainable energy future by deploying renewable energy technologies. However, the various barriers impede the development of renewable energy technologies. Therefore, this study identifies the seven-renewable energy barriers and twenty-nine sub-barriers, which obstruct the development of renewable energy technologies in Pakistan. Then, this study proposes various strategies to overcome these renewable energy barriers. First, the Analytical Hierarchy Process method assesses and prioritizes renewable energy barriers and sub-barriers. Then, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution method evaluates the strategies for sustainable implementation of renewable energy technologies. The results of the AHP method reveal that “Economic & Financial,” “Political & Policy,” and “Market” are ranked as the most critical barriers in the deployment of renewable energy technologies. Besides, the results of the Fuzzy TOPSIS method indicate that “Capital Subsidies,” “Feed-in Tariffs,” and “Direct, Enabling, & Integrating Policies,” are the most feasible strategies to overcome these barriers for the successful implementation of renewable energy technologies in Pakistan. This study shall assist the governments and policymakers in focusing their future efforts in deploying renewable energy technologies for sustainable energy planning and development.

The transition to a green economy for any country is crucial for the sustainability development of the economy, environment, society, and governance (EESG). A green economy is a sustainable approach to combating climate change and promoting sustainable development through the adoption of sustainable energy. This research utilizes the Delphi and fuzzy analytical hierarchy process (AHP) methodologies to assess and rank the EESG criteria and policy options for transitions to a green economy. The factors and policy choices are identified using the Delphi approach for further analysis. These factors and policy alternatives for switching to a green economy for sustainable development are determined using the fuzzy AHP technique. According to the fuzzy AHP approach, environmental, social, and economic factors are best suited for green economy transitions. The findings also show that the most crucial policy options for the switch to a green economy are research and development, carbon pricing, and renewable energy targets. The findings are useful to guide the creation of policies and the selection of options for the implementation of a green economy. Finally, the conclusion can guide actions to combat climate change and develop a sustainable energy future.

China has become a global leader in green infrastructure finance, investing heavily in renewable energy, sustainable transportation, and green buildings. However, there are multiple risks and challenges that impede the development of green infrastructure finance. Thus, this study analyzes and prioritizes the risks associated with green infrastructure finance in China and proposes policy plans to mitigate these risks. A Fuzzy analytical hierarchy process (AHP) is used to identify the main risks associated with green infrastructure finance. The main risks are further decomposed into sub-risks. After, the Fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is used to prioritize the key policy plans to mitigate risks and sub-risks. The results of Fuzzy AHP show that policy and regulations are the most significant risk associated with green infrastructure finance in China, followed by financial risks, and technical risks. The results of Fuzzy VIKOR reveal that increasing the availability of financing options is the most crucial policy plan to mitigate the risks and sub-risks for green infrastructure finance. The developed standardized technical guidelines and procedures and a legal and regulatory framework are ranked second and third are the most effective and feasible policy plans.

One of the challenging tasks for governments is to curtail the production and consumption of single-use plastic bags. Despite multiple advantages of plastic and its ubiquity, the environmental damage of single-use plastic bags is irreversible. However, policy intervention by the governments is also a need of the time to reduce its production and consumption. This study provides a strategic analysis of government policy options regarding the ban on single-use plastic production in Pakistan. A game-theoretic model is developed to examine the behavior of plastic bag producers vis-a-vis the government’s climate change policy. The study concludes that game theory is very effective in analyzing the efficient promulgation and productive implementation of environmental management policy regarding banning single-use plastic in Pakistan.

The COVID-19 pandemic needs immediate solution before inflicting more devastation. So far, China has successfully controlled transmission of COVID-19 through implementing stringent preventive measures. In this study, we analyze the effectiveness of preventive measures taken in thirteen regions of China based on the feedback provided by 1135 international students studying in China. The study uses factor analysis combined with varimax rotation of variables. It was found that awareness raising and dispersing actionable knowledge regarding trust and adapting measures remained significantly important. Therefore, recognition of information gaps, improvements in the level of alertness, and development of preventive measures in each sector are imperative. The findings of this study revealed that trust, students’ health, waste disposal, and the efforts of the Chinese government/international institute of education to prevent this pandemic were significantly and positively associated with preventive measures. The results showed that prior knowledge, global pandemics, and food and grocery purchases were firmly related to the preventive measures of COVID-19. Moreover, anxiety, transportation, and economic status were negatively related to the preventive measures. During this epidemic situation, international students suffered various types of mental stresses and anxiety, especially living in most affected regions of China. The study adopted a mixed (qualitative and quantitative) approach where the findings can act as a set of guidelines for governmental authorities in formulating, assisting in the preparation, instructing, and guiding policies to prevent and control the epidemic COVID-19 at national, local, and divisional levels. Electronic supplementary material The online version of this article (10.1007/s11356-020-10932-8) contains supplementary material, which is available to authorized users.

The literature on the impact of policy uncertainty on climate change has grown rapidly in recent years as policymakers and researchers have become increasingly concerned about the potential adverse effects of policy uncertainty on environmental sustainability. This study aims to investigate the impact of economic policy uncertainty (EPU), GDP per capita, renewable energy consumption (REC), and foreign direct investment (FDI) on environmental sustainability from the perspectives of the environmental Kuznets curve (EKC) and pollution halo/haven hypotheses. The research employs panel data analysis techniques, including panel corrected standard errors (PCSE) and generalized least squares (GLS), to analyze the data from a panel of 19 developed and developing countries from 2001 to 2019. The results reveal that EPU, GDP per capita, REC, and FDI significantly impact GHG emissions, contributing to climate change. The results of the study confirm a U-shaped EKC and pollution haven hypothesis in the selected economies. The findings of this study provide valuable insights for policymakers, as they highlight the need to consider the interplay between economic growth, foreign investment, and environmental policy in addressing climate change. The results also suggest that reducing policy uncertainty and promoting sustainable economic growth can mitigate the effects of climate change and ensure environmental sustainability.

Sustainable supply chain management (SSCM) has been a tough challenge for developing economies like Saudi Arabia. Implementation of SSCM practices in the manufacturing industry has been prone to multiple risk factors that need to be identified, evaluated, and prioritized especially considering the dynamics of the manufacturing industry in a developing economy. Moreover, it is also imperative to trace out feasible and sustainable strategies to overcome the risks to SSCM practices adoption. This study serves this purpose and identifies, evaluates, prioritizes the risk factors, sub-factors, and strategies to overcome these risk factors in the implementation of SSCM practices in the manufacturing industry in Saudi Arabia. An integrated multi-criteria decision analysis approach by combining fuzzy AHP and fuzzy WASPAS methods is employed for the analyses. The fuzzy AHP analysis results show that economic risks are dominant risks followed by the managerial policy risks and environmental risks in implementing SSCM. Industrial emissions are the leading risk factors in the overall ranking of the sustainable supply chain sub-risk factors followed by market dynamics, management policy failures, financial constraints, and credit uncertainty. While evaluating the sustainable supply chain strategies using fuzzy WASPAS, it is concluded that commitment and support of top, middle, and lower level management is the most pivotal strategy to deal with the risks to SSCM in Saudi Arabia followed by establishing environmental policies and goals to adopt SSCM, and provision of the financial resources and subsidies.

Carbon emissions have turned out to be one of the key alarming and complex issues which drive a long-lasting debate over climate change. The increasing trend in the usage of fossil fuels for the curse of economic development and at the same time reducing carbon emissions has become a significant phenomenon worldwide. In this study, we evaluate carbon emissions (CO2) during 1972-2016 by employing logarithmic mean Divisia index (LMDI) method. The results from decomposition using LMDI method indicate that the economic development factor is the main driving force for the increase of per capita carbon emissions in the country; the energy structure and energy efficiency are the restraining factor for per capita carbon emissions. Therefore, Pakistan should continue to upgrade energy structure from traditional sources to renewable energy sources to curb the increase of carbon emissions, and also, improve the efficiency of energy use and save energy to cope with environmental challenges. Finally, the study concludes with some policy suggestions.

Purpose This study aims to examine whether corporate social responsibility (CSR) activities influence firm performance based on a longitudinal survey for small and medium-sized enterprises (SMEs) in Pakistan. Empirical studies suggest that the SME sector plays an essential role in the economic development of Pakistan and can be considered the backbone of the economy. Design/methodology/approach The data for this study were collected from SMEs located in the cities of Karachi, Lahore and Faisalabad in Pakistan. A well-designed questionnaire was administrated over 240 entrepreneurs to analyze and measure the impact of CSR on financial performance for a 12-month period. The authors used econometric analysis of the data using structural equation modeling. Findings Results reveal significant relationships between CSR and two determinants of firm performance, namely, employee commitment and corporate reputation. Research limitations/implications Findings of the study are important for policymakers, entrepreneurs and other professionals in SMEs sectors both in under-developed and, with further application and exploration, in developing countries. Originality/value There is no single longitudinal study prior to this has been carried out on the relationships of CSR and firm performance in the SME sector in the context of the Pakistani economy. Hence, this study significantly fills an important gap in the research.

The study investigates the short-run and long-run relationship between oil price fluctuation and real sector growth in Pakistan. Four major sectors of the economy (Manufacturing, electricity, transport and communication, and livestock) were analyzed to find any relation. Similar studies can be found in the existing literate, however, the distinguish feature of present study is that it investigates each individual sector’s linkage to oil price changes. Annual time series data of selected sectors ranging from 1976 to 2017 is selected for the study. Classical normal linear regression models under auto regressive distributed lag (ARDL) were employed to study the relationship between economic sectors and oil price fluctuation. Empirical results indicate that changes in oil price adversely affect manufacturing, livestock and electricity sectors in short-run and long-run, while significant positive impact was found on transportation and communication. Consequently, the sectors prone to oil price changes require special attention of policy makers. An expansionary monetary policy can be a short-run solution to reduce the impact of increasing oil price, whereas the government can introduce a policy framework to counter this effect in long-run. Keywords: Oil price volatility, Real economic sectors, Economic growth, ARDL, Pakistan

Pakistan has an enormous amount of renewable and sustainable energy sources. However, it is unfortunate that the country remains unsuccessful in using these sources to deliver increasing electricity demand. Studies attribute various barriers to this failure. However, little is known about the degree to which these barriers impede renewable energy (RE) in Pakistan. Therefore, the current study presents a systematic framework to prioritize barriers according to their importance. The study is divided into three phases. Firstly, barriers were identified using literature survey. Secondly, the Modified Delphi method was used to finalize and categorize barriers in the Pakistani context. Twenty-one barriers were finalized, which were then categorized under five main barriers, including, political and regulatory, market competitiveness, institutional, technical, and social. Finally, Fuzzy Analytical Hierarchical Process (FAHP) was employed to calculate weights and rankings of barriers and sub-barriers. The results revealed that political and regulatory barrier category ranks highest among major categories, whereas, overall ranking of barriers showed that political instability sub-barrier is more important than the rest of twenty sub-barriers within different categories.

Accurate forecasting is required to measure future national energy performance levels in order to establish clear policies for both monitoring and reducing Nitrous Oxide and other harmful emissions. Using the well-established and accepted measures, we predict the Nitrous Oxide emissions for the year 2030 based on actual data from the years 2000 to 2016 for six countries responsible for 61% of global emissions (China, Indonesia, India, Japan, Russia and the USA). Three advanced mathematical grey predictions models were employed, namely the Even Grey Model (1, 1), the Discrete Grey Model (1, 1) and the Non-homogeneous Discrete Grey Model, which is capable of working with poor or limited data. Results showed that the Non-homogeneous Discrete Grey Model was a better fit and proved more effective in forecasting Nitrous Oxide emissions because it produced the lowest mean absolute percentage error for all countries when compared to the Even Grey Model (1, 1) and the Discrete Grey Model (1, 1). The mean absolute percentage error of the Even Grey Model (1, 1) was 2.4%, that of the Discrete Grey Model (1, 1) was 2.16%, and that of the Non-homogeneous Discrete Grey Model was 1.9%. Furthermore, the results show that China has the highest Nitrous Oxide emissions during the years studied (China 20,578,144, Russia 1,705,110, India 7,806,137, Indonesia 3,405,389, USA 8,891,219 and Japan 780,118). This study also suggests some implications for both academicians and practitioners in respect of reducing Nitrous Oxide emission levels.

The historical megacities act as a catalyst for sustainable economic development. But the rampant urbanization and demand for the development of mass-transit infrastructure have put pressure on natural environment and heritage scape. Collaborative urban planning is essential especially in culturally and archaeologically rich cities. Megaprojects such as mass-transit systems, especially in developing countries, normally are encouraged for political point scoring. But the choices of the mod of mass-transit systems, their route selection, design and vertical alignment may have a devastating impact on cultural and historical heritage leading to serious conflicts during execution of mass-transit plans. This study employs system engineering decision analysis techniques to resolve urban planning issues of heritage sustainability while analysing metro-heritage conflict in Lahore. Realizing the fact that the attitude(s) of the Decision Makers (DMs) play important role in the decision–decision making, the authors use the attitudes of the DMs based on options in Graph Model for Conflict Resolution (GMCR) to resolve the metro-heritage conflict.

As lockdown eases, economic activities resume in Pakistan. If the country continues to follow business-as-usual (BAU) then it is anticipated that carbon output could surge past pre-COVID-19 levels – that means more disasters in future. Thus, it is an unprecedented opportunity to shift from BAU and achieve carbon-neutral and nature-positive economic recovery – green economic recovery (GER). To fuel the GER, access to modern, equitable, affordable and sustainable energy is paramount. This study explores waste-to-energy (WtE) as an alternative green fuel for GER. Seven WtE technologies are prioritized based on the concept of energy trilemma – energy security, energy equity, and environmental sustainability. For the evaluation, an energy trilemma based decision support framework is developed using most prominent multi-criteria decision-making (MCDM) methods. The fuzzy set theory is integrated with MCDM methods to minimize uncertainty in results. Sixteen experts are engaged to score each WtE technology with respect to every energy trilemma dimension and sub-dimension. Gasification technology is found to be the most feasible option for WtE generation in Pakistan whereas Torrefaction technology is least favorable. It is concluded that the need to shift towards sustainable energy is more than ever to limit the carbon emission and prevent future crisis., Highlights − COVID-19 offers unprecedented opportunity for transition to renewable energy. − Green economic recovery is vital to limit carbon level and prevent future crisis. − Energy trilemma based decision support framework is proposed. − Waste-to-Energy can fuel economic recovery as well as can help cities keep clean. − Gasification is the most feasible Waste-to-Energy technology in Pakistan.

This research examines the heterogeneous dynamic links among healthcare expenditures, land urbanization, and CO

This research investigates the dynamic interactive associations among sustainable investment in the energy sector, air pollution, and sustainable development. To this end, it employs a “one-step” system-generalized method of moments (GMM) and “one-step” differential-GMM estimators, covering the period between 1996 and 2017. In this context, it utilizes the simultaneous equations of the dynamic panel data model for panel data of 27 Chinese provinces and municipalities. We have developed a new model of sustainable development, which incorporates sustainable investment in the energy sector and air pollution to offer a robust theoretical foundation for considering the underlying relations. The system-GMM estimator is used for the full data set; however, differential-GMM is utilized for the subsets of data, in order to tackle the small sample bias problem. The empirical outcomes provide several vital insights in that they yield mixed findings for the aggregated sample and subsets of data. For example, a two-way causal relationship occurs for all the panels, except the central part (medium development regions), between sustainable investment in the energy sector and sustainable development. Contrary to this, causality runs from air pollution to sustainable investment in the energy sector in a full data set and the central part (medium dev.). Nevertheless, the opposite is true in the case of the eastern part (most developed regions) of China. Still, the same relationship runs in either direction in the case of the western part (least developed regions). On the other way around, the feedback hypothesis of causality is confirmed, across all the samples, between air pollution and sustainable development. Hence, sustainable development and air pollution are overwhelmingly interdependent, in the country as well as the province and municipality level of the Chinese economy.

Two-sided matching problems are common and crucial parts of human activity; the psychological behavior of agents is an important factor that should not be ignored in such problems. However, existing academic research has not considered the psychological behavior of agents in two-sided matching models. Therefore, this study develops a method for determining the most satisfactory results for agents on both sides of a matching problem that considers agents’ regret aversion behavior as measured using linguistic preference information. Initially, the preference utility and regret values are computed based on regret theory; then, the perceived utility values of each agent are obtained. This two-sided matching model has been established to maximize matching satisfaction without waste using the minmax method. Finally, a practical example is discussed to demonstrate the feasibility and validity of the proposed method.

Reliability and quality are the two ultimate objectives in the avionics industry. The risk of counterfeit electronics and the unavailability of screening facilities for 100% components are the most concerning areas in the supply chain of the avionics industry. Unlike most public procurement, the cost is not the only significant criterion. Unbiased decision-making criteria to accommodate all the important factors without compromising on quality, reliability, and maintainability are essential for the evaluation and selection of sustainable suppliers. Therefore, this study proposes an unbiased decision methodology based on the fuzzy analytic hierarchy process (FAHP) and the fuzzy technique for order performance by similarity to ideal solution (FTOPSIS). In the first phase, six main and twenty-one sub-criteria are selected from the literature and empirically validated by experts of the avionics industry. FAHP is used to evaluate the weight of the main criteria and sub-criteria. FTOPSIS is used to prioritize eight alternatives (suppliers) concerning their effectiveness and superiority in finding the best alternatives. The results of the FAHP reveal that traceability (T) is the most important criterion, followed by quality (Q), and cost (C), which rank as the second and third most significant criteria. The results of the FTOPSIS rate supplier 8, supplier 2, and supplier 1 as the first, second, and third most effective suppliers, respectively.

Globally, organizations are under enormous pressure to implement green supply chain processes due to growing environmental concerns. Subsequently, organizations and firms have become more conscious of their suppliers&rsquo, green innovation ability. However, the selection of the most optimum supplier concerning green innovation ability remains a challenging task that needs to be analyzed. Thus, this study develops an integrated fuzzy and grey-based methodology to analyze and prioritize suppliers for small and medium enterprises (SMEs) in the context of Saudi Arabia. Initially, the study identifies 4 criteria and 20 sub-criteria through extensive literature review with respect to suppliers&rsquo, green innovation ability. Later, the Fuzzy Analytical Hierarchy Process (AHP) computes weights of criteria and sub-criteria. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)-Grey was employed to rank the suppliers. The process of assigning weights to criteria and sub-criteria involved twelve experts from academics and industry. The results of Fuzzy AHP indicated that the &ldquo, Green Innovation Initiatives&rdquo, is the most significant criterion for the supplier selection. The results of TOPSIS-Grey revealed that the &ldquo, Supplier-3&rdquo, is the most optimum supplier having the highest potential of adopting green practices among other suppliers. The overall results provide adequate feedback for organizations and firms to maximize their ability to curb environmental impacts from their upstream activities.

The establishment of Special Economic Zones (SEZs) is a lengthy, expensive, and long-term orientated endeavor. Proper selection of SEZs is indispensable to meet the objectives of export-led growth and value up-gradation. Consideration of sustainability issues in such planning under the Zone 3.0 paradigm is critical to achieve Sustainable Development Goals (SDGs) by 2030. Multiple key factors such as location, linkages, labor force, suitability of industries, incentives and facilitation, and market orientation are important in decision-making process of establishing SEZs. Furthermore, environmental conditions and resource availability need to be considered in the planning and policy making processes to keep symmetry in the natural environment and ecosystem of the areas under consideration for SEZs. The present study uses Multi-Criteria Decision Analysis (MCDA) methods in the perspectives of green industrial zone planning and development in Pakistan under the flagship project of China-Pakistan Economic Corridor (CPEC) of China&rsquo, s Belt and Road Initiative (BRI). This research uses Delphi method, Analytical Hierarchy Process (AHP), and the Fuzzy Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR). The Delphi method has been used to identify the main criteria, sub-criteria, and their weights for 3 SEZs under consideration. The results of AHP analysis unfolded that the majority of the experts believe the location and land aspect is the most pivotal criteria in setting SEZs followed by linkages, subsidies, and facilities criteria. Finally, the results of Fuzzy VIKOR analysis considering environmental sustainability reveals that Faisalabad SEZ is the best suited under given criteria and sub-criteria.

Carbon emissions have turned out to be one of the key alarming and complex issues which drive a long-lasting debate over climate change. The increasing trend in the usage of fossil fuels for the curse of economic development and at the same time reducing carbon emissions has become a significant phenomenon worldwide. In this study, we evaluate carbon emissions (CO

The widespread penetration of hydrogen in mainstream energy systems requires hydrogen production processes to be economically competent and environmentally efficient. Hydrogen, if produced efficiently, can play a pivotal role in decarbonizing the global energy systems. Therefore, this study develops a framework which evaluates hydrogen production processes and quantifies deficiencies for improvement. The framework integrates slack-based data envelopment analysis (DEA), with fuzzy analytical hierarchy process (FAHP) and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS). The proposed framework is applied to prioritize the most efficient and sustainable hydrogen production in Pakistan. Eleven hydrogen production alternatives were analyzed under five criteria, including capital cost, feedstock cost, O&amp, M cost, hydrogen production, and CO2 emission. FAHP obtained the initial weights of criteria while FTOPSIS determined the ultimate weights of criteria for each alternative. Finally, slack-based DEA computed the efficiency of alternatives. Among the 11, three alternatives (wind electrolysis, PV electrolysis, and biomass gasification) were found to be fully efficient and therefore can be considered as sustainable options for hydrogen production in Pakistan. The rest of the eight alternatives achieved poor efficiency scores and thus are not recommended.

Hydrogen can play a crucial role in increasing energy security and reducing greenhouse gases in Pakistan. Hydrogen can only be a clean and sustainable fuel if it is generated from renewable energy sources (RES). Thus, it is important to evaluate viability of RES for hydrogen production. This study developed a two-stage fuzzy MCDM (Multi-criteria decision-making) approach to select the most efficient RES. In the first stage, fuzzy analytical hierarchy process (AHP) obtained the relative weights of four criteria for the selection of best RES. These criteria included commercial potential, environmental impacts, economic benefits, and social acceptance. In the second stage, data envelopment analysis (DEA) measured the relative efficiency of RES using weights of criteria as outputs, and the cost of RES-based electricity generation as input. The results indicated that wind and solar are the most efficient sources of hydrogen production in Pakistan. Municipal solid waste (MSW) and biomass can also be considered a feedstock for the hydrogen economy. Geothermal reported to be the less efficient source and thus is not recommended at present. Sensitivity analysis confirmed the robustness of results obtained using the developed framework.

The off-grid solar photovoltaic (PV) system is a significant step towards electrification in the remote rural regions, and it is the most convenient and easy to install technology. However, the strategic problem is in identifying the potential of solar energy and the economic viability in particular regions. This study, therefore, addresses this problem by evaluating the solar energy potential and economic viability for the remote rural regions of the Sindh province, Pakistan. The results recommended that the rural regions of Sindh have suitable solar irradiance to generate electricity. An appropriate tilt angle has been computed for the selected rural regions, which significantly enhances the generation capacity of solar energy. Moreover, economic viability has been undertaken in this study and it was revealed that the off-grid solar PV power generation system provides electricity at the cost of Pakistani Rupees (PKR) 6.87/kWh and is regarded as much cheaper than conventional energy sources, i.e., around PKR 20.79/kWh. Besides, the off-grid solar PV power generation system could mitigate maximum CO2 annually on the condition that all of the selected remote rural regions adopt the off-grid solar PV system. Therefore, this study shall help the government to utilize the off-grid solar PV power generation system in the remote rural regions of Pakistan.

Pakistan has an abundant solar power potential which can be effectively utilized for the electricity generation. There are various sites across the country which have sufficient solar irradiation across the year, and thus, suitable for the installation of solar photovoltaic (PV) power projects. This study, therefore, aims to undertake research on the establishment of solar power project site selection in Pakistan. In this context, 14 promising cities of Pakistan are considered as alternatives and studied in terms of economic, environmental, social, location, climate, and orography criteria and further supplemented with 20 sub-criteria. Initially, the analytical hierarchy process (AHP) method has been used to prioritize each of the main criteria and sub-criteria. Later, fuzzy VlseKriterijuska Optimizacija I Komoromisno Resenje (F-VIKOR) method has been employed to prioritize the 14 alternatives. The present investigation reveals that Khuzdar (C2), Badin (C3), and Mastung (C7) are the most suitable cities for the installation of solar PV power projects in Pakistan. Finally, the outcome of the sensitivity analysis revealed that obtained results are reliable and robust for the installation of solar PV power projects in Pakistan. This study shall assist government, energy planners, and policymakers in making cities sustainable by establishing solar power projects in Pakistan.

This study aims to explore the potential of renewable energy resources to attain a 100% renewable electricity system in Pakistan. Currently, most of the electricity supply comes from fossil fuel, which is imported because Pakistan lacks its own resources. The imports of fossil fuel cost a huge amount and therefore afflict the already fragile economy. Further, the policy to rely on fossil fuel has significantly failed to address the energy crisis that has been lingering for the past two decades, and an acute shortage of electricity hinders the progress of various sectors of the economy. In addition, the global climate index has listed Pakistan among the top 10 climate-vulnerable countries, which makes it pertinent for the country to take precautionary measures for climate change mitigation. Pakistan has abundant renewable energy resources, which are more than sufficient to meet 100% of the present and future electricity demands. On the basis of the existing literature and the dilemma of the energy crisis and climate vulnerability, this study argues that transition to a 100% renewable electricity system is not only an option but also an urgent requirement. Further, we list the potential barriers, in the context of Pakistan, and put forward the policy implications for a swift transition to an entirely renewable electricity system.

This research examines the heterogeneous dynamic links among healthcare expenditures, land urbanization, and CO2 emissions across the development levels of China. To this end, data of 27 Chinese provinces are considered from 1999 to 2018. Theoretically, this research developed a healthcare expenditures-augmented Stochastic Impacts of Regression by Population, Affluence, and Technology (STIRPAT) model to incorporate healthcare expenditures as a determinant of affluence. Empirically, this research established a system of simultaneous equations based on the healthcare expenditures-augmented STIRPAT model to estimate the links among the variables. As a pre-analysis, second-generation Westerlund cointegration is applied and found the long-term equilibrium association among the variables. The long-run estimations and short-run causality are done by employing dynamic common correlated effects mean group method (DCCEMGM) and Dumitrescu-Hurlin causality. A heterogeneous long-run equilibrium linkage is confirmed to exist among the variables of interest. Concerning the long-run estimates, firstly, the healthcare expenditures growth and land urbanization exhibited a bilateral positive link. Secondly, CO2 emissions and healthcare expenditures growth manifested the existence of a bilateral positive link. And thirdly, a unilateral positive (negative) link is revealed to exist from a linear term (squared term) of land urbanization to CO2 emissions. Concerning the short-run results, firstly, a bilateral causal bond exists between the land urbanization and healthcare expenditures growth. Secondly, a bilateral causal bond prevails between CO2 emissions growth and healthcare expenditures growth. Finally, a unilateral causal bond is operational from land urbanization to CO2 emissions growth. In terms of the nature of the link, the long-run findings are consistent across the data samples. However, considering the degree of influence, heterogeneity is confirmed across the development levels for both long- and short-run. It infers that relatively more (less) developed regions showed relatively strong (weak) influence. Based on empirical findings, relevant policies are recommended.

In the wake of Paris agreement, global economies have been putting their full efforts in mitigation and adaption to limit the global temperature within 2 °C higher than the pre-industrial level. Redirection of investment flows to renewable energy technologies (RETs) is paramount to achieve the goals set in global climate agreement. The Turkish government is determined to attract investment in RETs for cleaner energy production and sustainable socio-economic development in Turkey. However, it is very challenging to substitute environmentally harmful traditional energy systems with renewable energy production and supply. Investing in and developing renewable energy projects (REPs) are posed to various inherent risks. Assessment and analysis of risk factors of renewable energy investments (REIs) are indispensable for a practical and profound policy guideline for mitigation. This study provides a Multi-Criteria Decision Methodology (MCDM) based three-stage decision framework to assess and examine the risk factors of REIs in Turkey. The first stage is the identification of risk factors of REI using the Delphi method. The second stage is the assessment of identified risk factors of REI by using the Analytical Hierarchy Process (AHP). The third stage is the evaluation and prioritization of strategies to overcome risk factors of REI projects by using Fuzzy Weighted Aggregated Sum Product Assessment (FWASPAS). The Delphi method unveiled 6 major risk factors with 23 sub-risk factors. Results of AHP analysis unfolded economic & business risk as a major risk factor. The FWASPAS analysis concluded a comprehensive and explicit explanation of the RET choices to be the most suitable of six strategies to deal with the investment risk factors for sustainable development in Turkey.

The world is facing the observable and direct impacts of climate change in the shape of extreme weather events, irregular precipitation, glacial melt, sea-level rise, an asymmetric average temperature trends, higher oceanic temperatures, damage to natural ecosystems, and biodiversity. In 2015, collective wisdom stirred the global community to agree upon two unprecedented milestones in recent human history—the Paris agreement and sustainable development goals (SDGs). The twin global agreements set a platform for collective efforts to secure socio-economic and climatic sustainability. Pakistan also joined the board to put forth policy interventions for the mitigation and adaption of climate change risks to maintain symmetry between the environmental quality and economic development. The government of Pakistan (GOP) formulated the National Climate Change Policy (NCCP (2012)) and passed the Pakistan Climate Change Act (PCCA (2017)) to provide an impetus for the implementation of the NCCP. Yet, an appropriate assessment and prioritization of the objectives of the NCCP are indispensable for productive and effective policies to achieve the targets. This study serves this purpose by prioritizing the climate change policy objectives (CCPOs) based on symmetric principles, i.e., energy, transport, urban and town planning, industry, and agriculture as criteria, with 17 sub-criteria in total. The fuzzy analytical hierarchy process (AHP) results show that the energy sector is the most pivotal while implementing the NCCP. Energy production is the most critical area that needs mitigation policy intervention for shifting the energy mix of the economy from high-carbon to low-carbon energy. The fuzzy AHP analysis further revealed the railway, population and urbanization, air pollution, crop growing practices and techniques as the most important related to transport, urban planning, industry, and agriculture sub-criteria, respectively. The fuzzy VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) analysis showed the ranking of the CCPOs from high to low through the integration of policies, institutional capacity building, water security, natural resource management, natural disaster management, environmental financial structure development, and social sector development. The findings of the present research would be helpful for experts and policymakers to re-examine the NCCP and put forth action plans to achieve sustainable climate and SDGs.

Global human pollutant activities have raised greenhouse gas (GHG) emissions, which have directly affected the climate. Fossil fuel-based energy has brought a negative impact on the environment and is considered one of the largest sources of GHG emissions. It is envisaged that GHG emissions will increase in the future due to rapid population growth and industrialization. Thus, it is imperative to mitigate climate variability and reduce GHGs by adopting renewable energy (RE) sources for electricity generation. In this regard, the multi-criteria decision analysis (MCDA) process would serve the purpose of framing out energy policy to analyze these environmentally friendly energy sources. This study uses an integrated decision methodology&mdash, a combination of Delphi, fuzzy analytical hierarchy process (FAHP), and the fuzzy weighted aggregated sum product assessment (FWASPAS)&mdash, for the adoption of RE sources for electricity generation in Turkey. Initially, the study identified five main criteria and seventeen sub-criteria using the Delphi method. Then, the FAHP method was used to evaluate and rank the main criteria and sub-criteria. Finally, the FWASPAS method was used to assess and prioritize five major RE sources for electricity generation. The FAHP analysis indicated that political criteria are the most influential, followed by economic and technical criteria. Further, the FWASPAS method revealed that wind energy is the most significant option for electricity generation. This decision-making process can help the energy planners to utilize RE sources for sustainable development.

Pakistan is facing a severe energy crisis since the last two decades, and yet the government has not been able to overcome this problem. Therefore, the various renewable energy resources of Sindh and Baluchistan province has been assessed to diversify the electricity generation from fossil fuels to renewable energy. In the study, Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis has been employed to assess the internal and external factors which affect the renewable energy technologies in Sindh and Baluchistan province. Then, the Fuzzy Analytical Hierarchy Process (Fuzzy AHP) method is used from the multi-perspective approach (i.e., economic, environmental, technical, and socio-political criteria). Therefore in the study, four criteria, seventeen sub-criteria and three renewable energy resources (i.e., solar, wind, and biomass) have been assessed as alternatives in the decision model. It is identified that economic and socio-political are the two most important criteria. Further results reveal that wind has ample potential to generate electricity in both (Sindh and Baluchistan) provinces, whereas solar and biomass energy ranked second and third, respectively. It is, therefore, suggested that the government should exploit renewable resources to mitigate the current energy crisis and increase energy security for sustainable development of the country.

Pakistan has sufficient wind energy potential across various locations of the country. However, so far, wind energy development has not attained sufficient momentum matching its potential. Amongst various other challenges, the site selection for wind power development has always been a primary concern of the decision-makers. Principally, wind project site selection decisions are driven by various multifaceted criteria. As such, in this study, a robust research framework comprising of factor analysis (FA) of techno-economic and socio-political factors, and a hybrid analytical hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) have been used for the prioritization of sites in the southeastern region of Pakistan. The results of this study reveal economic and land acquisition as the most significant criteria and sub-criteria, respectively. From the eight different sites considered, Jamshoro has been prioritized as the most suitable location for wind project development followed by Hyderabad, Nooriabad, Gharo, Keti Bandar, Shahbandar, Sajawal, and Talhar. This study provides a comprehensive decision support framework comprising of FA and a hybrid AHP and Fuzzy TOPSIS for the systematic analysis to prioritize suitable sites for the wind project development in Pakistan.

This study proposes an integrated methodology based on Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, Analytic Hierarchy Process (AHP), and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (F-TOPSIS) to evaluate energy strategies for sustainable energy planning. The SWOT analysis is employed to determine the factors and sub-factors essential for sustainable energy planning. Subsequently, AHP, a Multi-Criteria Decision Making (MCDM) method is used to determine the weights of each factor and sub-factor. Finally, F-TOPSIS method is used to rank the 13 energy strategies under the case study for Pakistan's to achieve sustainable energy planning. The results of the study reveal that providing low-cost and sustainable electricity to residential, commercial, and industrial sectors (WO5) is highly prioritized energy strategy. While the utilization of coal resources and transformations of same into electricity (WO2) is prioritized as least favored energy strategy. The sensitivity analysis of the results validates the robustness of the study. This study, as such, proposes a systematic approach to develop and evaluate energy strategies for the sustainable energy planning and policy endeavor of Pakistan.