Assessing the drivers and solutions of green innovation influencing the adoption of renewable energy technologies.

The degradation of the environment in China is accelerating along with economic expansion. Adoption of renewable energy technologies (RETs) is crucial for reducing the adverse impacts of economic growth on the environment and fostering sustainable development. This study attempts to identify the green innovation drivers and sub-drivers that affect the adoption of RETs in China and provide solutions for boosting their implementation. The study prioritized the drivers, sub-drivers, and strategies of green innovation by combining the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods. In the study, the triple bottom line (TBL) approach has been used to determine the economic, societal, and environmental driving forces. The study also suggests strategies for encouraging the use of RETs. The results of the AHP method revealed that economics is the most crucial driver, with a weight of 0.376, followed by environmental (0.332), and social (0.291) drivers. The findings of the SAW method indicated that government green innovation initiatives, consumer initiatives, and industry initiatives are the most significant strategies for deploying RETs in China. This study has important theoretical and practical ramifications for encouraging China to adopt RETs. The suggested approaches can help researchers, business professionals, and policymakers promote sustainable development in China.

Assessing and prioritizing drivers and strategies for transition to a green energy for sustainable development in China.

This study addresses the critical global challenge of climate change, primarily caused by the overconsumption of fossil fuels. Recognizing the urgent need for a transition to green energy (GE) sources such as wind, solar, hydro, and biomass, this research focuses on identifying effective strategies for fostering a sustainable and low-carbon energy future in China. The study employs a combination of the Analytical Hierarchy Process (AHP) and fuzzy Decision-making Trial and Evaluation Laboratory (DEMATEL), both well-established multi-criteria decision-making (MCDM) methods, to analyze various drivers, sub-drivers, and strategies crucial for this transition. Through an extensive literature review, we identified several key drivers and strategies aiding the shift towards GE for sustainable development. The AHP method was applied to evaluate and rank the major drivers and sub-drivers, such as policy, financing, and infrastructure and innovation, that are crucial for China's successful transition to GE. Simultaneously, fuzzy DEMATEL was utilized to prioritize vital strategies, including public awareness and education, financial incentives and support mechanisms, and policy and regulatory frameworks. The findings reveal that, in addition to strong policy and financial support, public awareness and education are critical for advancing GE development in China. This study underscores the importance of integrating various drivers and strategies for effective green energy development, aiming to mitigate the environmental impacts of fossil fuel use.

Examining and prioritizing the effect of sustainable energy on the job market to advance China's green workforce.

China, the world's greatest emitter of greenhouse gases, has committed to reaching peak carbon dioxide emissions by 2030 and carbon neutrality by 2060. One strategy to accomplish this aim is switching to a low-carbon economy via advancing renewable energy (RE) projects. Therefore, this study focuses on solar, wind, biomass, and hydropower and examines the beneficial employment effects of RE projects in China. The study uses fuzzy-based Multi-Criteria Decision Making (MCDM) methodologies, such as the Analytical Hierarchy Process (AHP) and Weighted Aggregated Sum Product Assessment (WASPAS), to evaluate numerous employment effect criteria and sub-criteria. Based on these evaluations, the report prioritizes four main types of RE projects. According to the fuzzy AHP technique results, direct employment, skill requirements, and local employment are the most crucial employment effect factors. The study then employed the fuzzy WASPAS approach to assessing various RE initiatives' employment prospects. According to the findings, hydropower is the best choice for creating jobs, followed by wind and solar power initiatives. In recent years, the hydro, wind, and solar power industries have experienced rapid expansion, creating numerous job possibilities in production, installation, operation, and maintenance. Additionally, creating RE projects can boost regional economic growth and lessen poverty.

Sustainability in Pakistan's textile industry: analyzing barriers and strategies for green supply chain management implementation.

The industries view green supply chain management (GSCM) as a viable means of achieving sustainable operations by reducing environmental impact and enhancing operational performance. Although conventional supply chains still dominate many industries, integrating eco-friendly practices through green supply chain management (GSCM) is crucial. Nonetheless, there are several barriers that hinder the successful adoption of GSCM practices. Therefore, this study proposes fuzzy-based multi-criteria decision-making approaches comprised of the Analytical Hierarchy Process (FAHP) and the Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS). The study evaluates and overcomes barriers to the adoption of GSCM practices in the textile manufacturing sector of Pakistan. After the comprehensive literature review, this study identifies 6 barriers, 24 sub-barriers, and 10 strategies. The FAHP method employs to analyze the barriers and sub-barriers. Then, the FTOPSIS method ranks the strategies to overcome various identified barriers. Based on the FAHP results, the most significant barriers to the adoption of GSCM practices are technological (MB4), financial (MB1), and information and knowledge (MB5). Further, the FTOPSIS indicates that increasing the research and development capacity (GS4) is the most vital strategy for implementing GSCM. The study's findings have important implications for policymakers, organizations, and other stakeholders interested in promoting sustainable development and implementing GSCM practices in Pakistan.

Analysis of core risk factors and potential policy options for sustainable supply chain: an MCDM analysis of Saudi Arabia's manufacturing industry.

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.

Energy trilemma based prioritization of waste-to-energy technologies: Implications for post-COVID-19 green economic recovery in Pakistan.

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.

Dynamic interactive links among sustainable energy investment, air pollution, and sustainable development in regional China.

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.

Stylized heterogeneous dynamic links among healthcare expenditures, land urbanization, and CO2 emissions across economic development levels.

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.

Regional disparities in Preventive measures of COVID-19 pandemic in China. A study from international students' prior knowledge, perception and vulnerabilities.

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.

Forecasting Nitrous Oxide emissions based on grey system models.

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.

Decomposing factors affecting CO2 emissions in Pakistan: insights from LMDI decomposition approach.

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.

Evaluating renewable energy sources for implementing the hydrogen economy in Pakistan: a two-stage fuzzy MCDM approach.

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.

A sustainable solution for electricity crisis in Pakistan: opportunities, barriers, and policy implications for 100% renewable energy.

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.

Assessing the solar PV power project site selection in Pakistan: based on AHP-fuzzy VIKOR approach.

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.