key: cord-1040683-9egrkqvs
authors: Yao, Shuangliang
title: Fuzzy-based multi-criteria decision analysis of environmental regulation and green economic efficiency in a post-COVID-19 scenario: the case of China
date: 2021-02-16
journal: Environ Sci Pollut Res Int
DOI: 10.1007/s11356-021-12647-w
sha: dee1531feb773d7e583c91568d4bbbe534793460
doc_id: 1040683
cord_uid: 9egrkqvs

The COVID-19 pandemic outbreak posed serious threats not only to global health but also to the worldwide development regime. The experts, economists, policymakers, and the governments expressed their pledges and determinations to adapt and mitigate climate change. Policymakers and governments have started adopting green growth and development strategies. The progress moves further to achieve green economic efficiency (GEE) to achieve economic, social, and environmental development. One of the major challenges has been promulgating and strictly implementing environmental regulations and policies vis-à-vis green growth and development. China, having the second largest economy, has started its voyage to achieve GEE. However, there are multiple challenges on the way to the green economy. The objective of the present stud is to analyze environmental regulation and GEE in China using fuzzy-based multi-criteria decision analysis. To serve this purpose, the study identifies 5 alternative strategies to achieve GEE while considering 10 criteria and 48 sub-criteria in the context of environmental regulations in China. The Fuzzy Analytical Hierarchy Process (AHP) has been employed to rank criteria and sub-criteria to the goal. The Fuzzy VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method has been used to rank the alternative strategies of GEE. The proposed model unveiled resource efficiency and green purchasing as the best strategy to achieve GEE in the Chinese economy followed by local production. The study provides a comprehensive insight into the green development process to achieve GEE in the Chinese economy in the post-COVID-19 world.

In the wake of the Paris agreement and the global consensus on Sustainable Development Goals (SDGs) in 2015, the global economies have been focusing on the formulation and implementation of adaption and mitigation strategies (Ahmed et al. 2020a ). However, economic development and environmental protection being contradictory objectives have become a major concern, especially for developing countries (Liu et al. 2020) . Unprecedented rapid growth in developing economies required resource inputs resulting in significant ecological damage (Miao et al. 2017) . Promulgation and enforcement of environmental regulations have fundamental importance for eco-efficiency to balance the economic value and environmental consequences (Liu et al. 2020) . The economists, policymakers, and economic decision-makers are convinced that the greening of economies is not a "drag on growth" but a new "engine of growth" (UNEP 2011) . It provides a great opportunity and motivates the policymakers and the governments to enable conditions to stimulate transitions to a green economy.

Green Economic Initiative (GEI) was founded by the United Nations Environment Programme (UNEP) in 2008; it was aimed to improve "human well-being and social equity, while significantly reducing environmental risks and ecological scarcities" (UNEP 2011) and the green economy was defined as "one which is a low carbon, resource-efficient, and socially inclusive" (UNEP 2011) . The concept of GEE is multi-dimensional and multi-faceted. It must be examined from the perspectives of a multi-criteria decision framework. As the economic efficiency is not only limited to GTI, its scope is beyond it, extending to every socio-economic activity in the economy. Recently, green economic efficiency (GEE) has attracted the attention of researchers and analysts. A recent study by Liu et al. (2020) traced out the causal relationships among environmental regulation, green technological innovation (GTI), and their interaction term on eco-efficiency in China. The study explores the impact of GDI on ecoefficiency under multiple environmental regulations to adjust and obtain the more marginal benefit of green technology. Based on the economic-social-resource-environment perspective, Wang et al. (2019) examine GEE in the Yangtze River Delta (YRD) using spatiotemporal evolution and influencing factors. The study unveiled that overall spatial pattern has emerged higher efficiency in the West but lower efficiency in the East. The biggest direct effect on local GEE and spatial spillover effects on nearby areas was found in terms of the proportion of tertiary industry. Though the study was primarily focused on environmental regulation, GTI and ecoefficiency yet did not cover the overall dynamics of environmental regulation and green economic efficiency (GEE) in China.

Global efforts were underway to combat the menace of climate change. It was considered to be a global climate challenge in the pre-COVID-19 world. The COVID-19 pandemic outbreak changed the overall scenario and diverted the attention of the policymakers and the governments toward an emergent and unprecedented global health crisis posed by a novel coronavirus. COVID-19 appeared as a development challenge for all countries, accentuating the case for a global development paradigm. The pandemic has implications across global value chains, digitalization, debt, and climate change (Oldekop et al. 2020) . Efforts have been put to enhance the energy landscape and reduce air pollutions (Solangi et al. 2018) . Global oil market crash and COVID-19 pandemic exerted economic and social issues that need to be addressed through thoughtful policy design (Henry et al. 2020 ). There is a dire need to be persistent in the efforts of mitigation and adaptation of climate change. Though the pandemic has affected the growth and development trajectories of the economy, yet an economic recovery tilted toward green stimulus and reduction in traditional fuel investment would add the positive outcomes in avoiding future global warming (Forster et al. 2020) . The Chinese economy, being first hit by the COVID-19 pandemic, is focusing on economic recovery. The focus is on the stimulation of investment in infrastructure to facilitate renewable energy, intercity transport, and electric vehicle charging stations. Moreover, the Chinese government, in post-COVID-19 recovery, is focusing on five work streams-food, water and energy, urban climate adoption, circular economy, energy storage (Thorpe 2020) . All of these areas are pivotal to achieve environmental regulation and GEE criteria and alternative strategies.

The objective of the paper is to assess and prioritize alternative strategies to achieve GEE in China in a post-COVID-19 scenario. This study prioritizes 5 alternative strategies under 10 criteria and 48 sub-criteria in total. These criteria and sub-criteria are distilled through extensive literature review in environmental regulation and policy formulations for green economic development and green economic efficiency achievement in the Chinese economy. This study is a comprehensive analysis based on the fuzzy-based multi-criteria decision analysis. Firstly, the identified criteria and sub-criteria for green economic efficiency are assessed and ranked using the Fuzzy Analytical Hierarchy Process (AHP). Secondly, green economic efficiency strategies are evaluated and prioritized using the Fuzzy VIKOR 1 method. Rankings of each criterion and sub-criteria are found based on Fuzzy AHP estimations. This ranking clarifies the important criteria and sub-criteria significant for green economic efficiency. Moreover, the Fuzzy VIKOR analysis provides the ranking of GEE alternative strategies in the context of prioritized criteria and subcriteria for the Chinese economy. The fuzzy-based multicriteria decision analysis for environmental regulation for green economic efficiency would provide guidelines in the post-COVID-19 scenario.

Proposed criteria, sub-criteria, and alternative strategies to green economic efficiency

The present study focuses on the examination and prioritization of strategies to GEE in China based on the fuzzy-based multi-criteria decision analysis of environmental regulations for GEE. Environmental regulation is an important fulcrum for the transition to green industrial growth (Feng and Chen 2018) . The recent study provides a profound understanding by arguing that moderate environmental regulation can help reduce the damaging influence of green technological innovation (Liu et al. 2020) . The primary focus of this study is to identify, assess, and prioritize the criteria and sub-criteria for alternative GEE strategies in China. This research determines a profound insight to achieve GEE in the post-COVID-19. To serve this purpose, the authors have carried out an extensive literature review to identify criteria and sub-criteria for GEE in China. The study finalized 10 major criteria including Socio-Economic Development Policies, Green Growth Agenda promulgated by the government, Green Industrial Development (GID), Environmental Regulations, Resource Efficiency, Technological Initiatives and Innovation (TII), Green

The AHP method was proposed by Saaty in the 1970s (Saaty 1990; ). This approach is very famous and useful in analyzing and prioritizing any decision-making problem. The AHP method has four hierarchical stages: goal, criteria, sub-criteria, and alternatives (Solangi et al. 2019b ). This study used the Fuzzy AHP technique to obtain more reliable and robust results. Because fuzzy set theory helps in minimizing any human error and ambiguity during decisionmaking. In this study, the fuzzy-based pairwise comparison matrix has been operated as triangular fuzzy numbers (TFNs) to evaluate the GEE criteria and sub-criteria for environmental sustainability in China. The linguistic terms based on TFNs are shown in Table 3 (Kaganski et al. 2018) .

The following steps have been used to compute the inconsistency ratio of the fuzzy pairwise comparison matrix, which is proposed by Gogus and Boucher (1998) .

Step 1. Convert a triangular fuzzy matrix into two independent matrices:

Next, the first triangular fuzzy matrix is established with the middle fuzzy triangular matrix:

Here, the second triangular fuzzy matrix is established by a geometric mean approach for the upper and lower bounds of the TFNs:

Step 2. Establish the weight vector based on the Saaty method and computation of lambda max.

Step 3. Establish the consistency index (CI) for each matrix:

Step 4. Establish the consistency ratio (CR) for each matrix. For CR, the consistency index (CI) of each matrix is divided by its random index (RI).

The fuzzy matrices are to be considered valid if the values of CR m and CR g are smaller than 0.10. However, if the value surpasses 0.10, then it will be considered inconsistent. The RI scale is shown in Table 4 . The RI scale of the current study is proposed by Gogus and Boucher (Zhou et al. 2019 ).

The VIKOR method was proposed by Opricovic in 1980 (Opricovic and Tzeng 2007) . This approach mainly focuses (Hou et al. 2019) . Moreover, the increase in the local production of goods and services is also significant to achieve the objective (ICMA 2019). The Chinese government has initiated Agenda 2030 for the adoption and implementation of SDGs in China (Yu et al. 2020) . In addition to this, the Chinese government has introduced a new policy paradigm of "ecological civilization" (Tracy et al. 2017) .

Green Civil Society (GCS) Initiative(s) A12 The emergence of the green public sphere in the economic growth and development process plays a pivotal role in environmental protection (Yang and Calhoun 2007) . The GCS has been growing fast and playing its positive role in combating environmental degradation and climate change (Fengshi 2009; Yang and Calhoun 2007) . Ensuring Stakeholder Participation A13 The participation of stakeholders and their role in developing a stable modern society is indispensable (Laurisz 2019) . Stakeholder participation is beneficial in sustainable development (Weng et al. 2015; Zhuang 2017) .

Gender Mainstreaming A14 The objectives of sustainable development cannot be achieved until the mainstreaming of gender into economic development policies and projects. The development should offer more equal opportunities for both men and women in development (Bohong et al. 2016 ).

A15 Sectoral and regional development initiatives need to be introduced to ensure sustainable and green development in the economy. Though the Chinese economy has shown tremendous growth, yet initiatives are required to address the regional development strategy among the Eastern, Central, and Western regions in China (Chu et al. 2018; Weng et al. 2015) .

A16 In addition to the adoption of green stimulus for industries, investment in green projects, and other development initiatives, social inclusion indispensable in achieving the green economy objectives (IIED 2020; Weng et al. 2015) .

Green Growth Agenda Inclusive and Collaborative Planning B21 Inclusive and collaborative planning is fundamental to the development process (Ahmed et al. 2020b; Ali et al. 2019 ). In addition to this, communicative planning could help avoid conflicts during the development process .

Promote Green Growth Patterns (GGPs) B22 The promotion of GGPs is pivotal for green economy efficiency. The Chinese government has been focusing on adopting GGPs (Weng et al. 2015) .

Simulate Green Investment B23 Green investment is critical for GEE. If the Chinese government attracts the green inward FDI and invests in foreign technology-intensive industries, it will be helpful to obtain green technology spillovers and stimulate green innovation (Luo et al. 2021 ).

B24 It is essential to provide direct government funding and tax incentives to promote green technology innovation. Moreover, it is also important to introduce some tax incentives to support green technology initiatives (Guo et al. 2018 ).

Sustainable Special Economic Zone (SSEZs) Development B25 The establishment of SSEZs is fundamental for setting up an eco-friendly industrial base in the economy. Apart from the other factors in developing SSEZs, sustainability issues under the Zone 3.0 paradigm are indispensable to achieve SDGs (Ahmed et al. 2020b ).

Green Product Innovation (GPI) C31 GPI refers to the product innovation that fulfills environmental requirements through innovation in design, development, and production through the product life cycle (Feng and Chen 2018) . It plays a pivotal role in setting the green growth trajectory of the economy. In GPI, mostly, the focus is on the reduction of energy consumption (Chan et al. 2016; Feng and Chen 2018) .

Environ Sci Pollut Res GCI refers to the "innovation of production technology and technological equipment in the production process" (Feng and Chen 2018) . It is believed that the increase in technical transformation investment of the total output value increases the GCI ability in the economy (Feng and Chen 2018) . Green Innovation Initiative (GII) for Green Industrial Growth (GIG) C33 It is beneficial to decouple economic growth from the use and consumption of natural resources and energy. Moreover, it is also important to provide more value with better economic and ecological efficiency. The government's support is indispensable in encouraging green innovation for GIG (UNIDO 2010) . Green innovation is also important in realizing sustainable development (Luo et al. 2021 ).

Industrial Specialization C34 Industrial specialization is conducive to economic growth (Ma et al. 2019 ).

Industrial Diversity C35 Industrial diversity is also an important factor in promoting innovation and productivity (Ma et al. 2019 ).

Industrial Competition C36 Industrial competition is conducive to improving capital productivity (Ma et al. 2019 ).

Administrative Environmental Regulations (AERs) D41 The AERs refer to the mandatory environmental laws, regulations, and policies promulgated and enacted by the government and government's environmental protection agencies. The Chinese government has introduced multiple AERs to ensure environmental protection (Feng and Chen 2018) .

Regulations (MERs)

D42 MERs refer to sewage charges, subsidies, and tradable permits to prevent environmental damage and reduce pollution. The Chinese government has also introduced multiple MERs to protect the environment (Feng and Chen 2018) .

System Development D43 Monitoring and evaluating system development are critical in setting growth and development. The Chinese government has focused on "strengthening cross-departmental coordination of environmental monitoring and local management system" (Weng et al. 2015) .

D44 Public participation environmental regulations are introduced to ensure public participation in environmental regulation activities through understanding information and compliance with environmental regulations (Feng and Chen 2018) . Land/Planning laws D45 Land use policies are a fundamental part of the policy mix to achieve economic, environmental, and social goals. Land use policies must ensure the cooperation between stakeholders at the local, regional, and national levels (OECD 2020). E5

Resource Efficiency Minimization of Environmental Risk E51 Minimization of environmental risks is one of the key objectives of the governments. It is critical to minimize the environmental risks posed by the industries, companies, and other business units in the economy. The Chinese government has introduced multiple policy instruments to manage environmental risks (Weng et al. 2015 (Weng et al. 2015) .

Direct Government Funding and Tax Incentives F61 Technological progress increases energy efficiency, reduces energy consumption, and has rebound effects and helps in energy saving (Liao F63 Promotion of R&D is fundamental in achieving a green economy (Weng et al. 2015) .

Green Technology Innovation F64 Green innovation is pivotal for the transition to green industrial growth (Cao et al. 2019 ).

Marketization Innovation F65 Marketization innovation is also important. The intervention policies from the central/provincial governments independently stimulate the adoption of marketization innovation (Zhu and Zhang 2019).

Green Energy Initiative G71 In the wake of the Paris agreement, governments worldwide have introduced green energy initiatives to reduce energy consumption and shift their energy mix from traditional to renewable energy sources (Kul et al. 2020 ). The Chinese economy has done impressively well in green energy production and consumption (Liao and Wang 2019).

G72 In addition to the production and consumption of clean energy, it is also vital to reduce energy consumption through energy-saving technology adoption ( Recycling Infrastructure H83 Recycling infrastructure is critical for green economic development. The Chinese government aims to increase the recycling rates for residence and to build basic trash sortation systems. The government is also considering charging waste management fees for residential and commercial waster in major cities (CLP 2020).

Transport Infrastructure H84 Transport infrastructure and its efficient use are critical for economic efficiency (Ma et al. 2019 ).

Green Buildings H85 The development and promotion of green buildings are significant in achieving SDGs. Chinese green building industry is growing fast (Fu et al. 2020 ) and will play a pivotal role in obtaining GEE. Moreover, urban green infrastructure has been given special attention in urban development planning and projects in China (Z. Zhu et al. 2019a ).

Air Pollution Control I91 Environmental protection laws are important for green economic development. The Chinese government formulated and enacted the "Air Pollution Prevention and Control Law of the PRC" (Guo et al. 2018 ).

Wastewater Management I92 Treatment of wastewater influent, sewage treatment, and water recycling are also important to pollution control and resource management. The Chinese government has put forward policy interventions to ensure sustainable water quality, maximizing energy recovery, efficient resource recycling, and environmental friendliness (Qu et al. 2019 ).

I93 SWM is one of the challenges for governments and societies. Efforts are needed to reach a zero-waste economy. It can be obtained through innovative technologies to reduce waste streams, increasing recycling on ranking the set of alternatives with conflicting criteria to obtain compromise solutions. This approach will help decision-makers reach a mutual consensus (Liu et al. 2013 ). This study utilized a fuzzy-based VIKOR method to obtain more significant and reliable findings, even with less information, vague, and uncertain. The fuzzy set theory was developed by Zedah who proposed it in 1965 (Solangi et al. 2019a ). There are several triangular fuzzy numbers (TFNs) that can be used for various purposes based on the decisionmaking problem (Shukla et al. 2014 ). (Ali Shah et al. 2020 ).

I94 The concept of a shared and circular economy has got attention all over the globe. The Chinese government has taken initiatives to adopt and promote a shared and circular economy in the country (Pesce et al. 2020; Zhu et al. 2019b ).

Skill Development J101 Labor policies need to ensure investment in skills required for a sustainable and low-carbon economy (ILO 2011). Occupational Safety and Health (OSH) J102 Insurance of occupational safety and health is also fundamental on the way to sustainable development (ILO 2011). SME Development J103 The development of SMEs can help ensure green jobs due to the labor-intensive (Ali 2013 ) and the environmentally friendly nature of SMEs (ILO 2011). Green Jobs J104 Sustainable development encompasses economic growth, sustaining environmental quality, and improving human health, social justice, and employment (Pociovălișteanu et al. 2015) . Green jobs refer to "the transformation of economies, enterprises, workplaces and labor market into a sustainable, low-carbon economy providing decent work" (ILO 2015). Green economic development strategy

The economies are industrializing by going green, using smart and innovation-driven ideas (Cao et al. 2019) . Economic growth and development are the primary objectives to achieve. In the green economy, like traditional economic development, strategies are adapted for growth and development that improve environmental outcomes (ICMA 2019).

Resource efficiency and green purchasing strategy

The green economic development strategies focus on production supply. Resource efficiency and green purchasing are broader strategies that address the consumption side of the green economy. These strategies harness the community's purchasing power, demand for energy, resources, water, and green products (ICMA 2019). S3

Local production and utilization strategy Local production and utilization strategy is another alternative to green development strategy. It encourages an increase in community wealth by producing and consuming locally. It promotes regional self-reliance and economic security. It also eliminates the environmental impacts linked to transportation and logistics (ICMA 2019; OECD 2012). S4

Waste stream management strategy This strategy primarily focuses on the adoption of aggressive solid waste management programs. It focuses on reducing the costs and negative externalities related to waste disposal. The local governments can do impressively by creating jobs and reducing the cost of doing business. However, for this purpose to achieve, innovative technologies need to be introduced for a reduction in the waste stream, an increase in recycling, and transforming W2E (ICMA 2019). S5

Green infrastructure strategy Green infrastructure development strategy is another alternative to achieve GEE (John et al. 2019) . Comprehensive and inclusive land use policy decisions can significantly help inefficient resource use, maintain environmental quality, and create economic activity. Moreover, local governments can positively impact the green economy by using their authority in planning and implementing. The local governments can help improve the quality of life for the residents and improve the local business environment (ICMA 2019).

The followings are the key steps of the Fuzzy VIKOR method:

Step 1. Create a decision matrix In this study, 48 sub-criteria and 5 alternatives are ranked based on the Fuzzy VIKOR method. Table 5 shows the type of criterion and weight assigned to each criterion.

The TFN scale of this study is presented in Table 6 . The alternatives in terms of various criteria are evaluated, and the results of the decision matrix are shown in Appendix 2 Table 22 .

Step 2. Determine the positive ideal solution and negative ideal solution

Positive and negative ideal solutions of each criterion can be obtained as follows.

If the criterion is positive, the positive ideal solution ( e f * ) and negative ideal solution ( e f°) can be obtained using the following relations: Averagely dominant (2, 3, 4) 4

Averagely to strongly dominant (3, 4, 5) 5

Strongly dominant (4, 5, 6) 6

Strongly to very strongly dominant (5, 6, 7) 7

Very strongly dominant (6, 7, 8) 8

Very strongly to extremely dominant (7, 8, 9) 9 Extremely dominant (9, 9, 9) 

If the criterion is negative, the positive ideal solution ( e f * ) and negative ideal solution ( e f°) can be obtained using the following relations:

Step 3. Compute the normalized decision matrix Based on the positive and negative ideal solutions, a normalized decision matrix can be calculated by the following relation:

Appendix 2 Table 23 shows the normalized values of the evaluation matrix.

Step 4. Compute the values e S i and e R i :

First, the normalized matrix is transformed into a weighted normalized decision matrix, and then the values e S i and e R i can be calculated as follows:

If 

Step 5. Calculate the VIKOR index (Q)

The value of Q can be calculated as follows. The variable v representing the maximum group utility is equal to 0.5 in this study.

The fuzzy numbers S, R, and Q can be transformed into crisp numbers using the following formula.

If e A ¼ l; m; r ð Þ ( e A is expressed as a fuzzy number)

Appendix 2 Table 24 shows the fuzzy values S, R, and Q.

Step 6. Proposing a compromise solution

In this step, a decision is made based on the values R, S, and Q for the alternatives sorted in descending order. Two conditions need to be made a decision, and a set of compromise solutions can be proposed following these two conditions. Condition 1. Acceptable advantage: Q(A (2) ) − Q(A (1) ) ≥ 1/(m − 1) where A (1) is the alternative with the first position and A (2) is the alternative with the second position in the ranking list by Q. m is a number of alternatives. Condition 2. Acceptable stability in decision-making:

The alternative A (1) must also be the best ranked by S or/and R.

If one of the conditions is not satisfied, then a set of compromise solutions is proposed, which consists of the following:

for maximum M (the positions of these alternatives are "in closeness"). Solution 2. Alternatives A (1) and A (2) if only condition 2 is not satisfied. Solution 3. The alternative with the minimum Q value will be selected as the best Alternative if both conditions are satisfied.

It is one of the tough jobs to assess the GEE criteria. The weights assigned by inexperienced individuals are always unimportant and useless in making decisions (Solangi et al. 2019c ). Therefore, this research consulted with six professional and experienced experts to assign weights of GEE criteria, sub-criteria, and alternative strategies using Fuzzy AHP and Fuzzy VIKOR methods. These experts belong to academia, research institute, economist, and ecologist. All the responses of experts were gathered by webmail service to analyze the obtained data using Fuzzy AHP method and Fuzzy VIKOR methodologies. Thus, the findings of this research would help in identifying the most suitable GEE criteria, sub-criteria, and strategy in the context of China.

In this section, the findings of 10 GEE criteria, 48 sub-criteria, and 5 strategies have been analyzed and ranked using Fuzzy AHP and Fuzzy VIKOR approaches. The GEE criteria and sub-criteria have been evaluated using the Fuzzy AHP method, whereas the Fuzzy VIKOR approach has been used to analyze the GEE strategies. These assessed GEE criteria and strategies are considered to be very crucial for the development and implementation of environmental regulation and GEE in the country. Therefore, the detailed findings are provided in the below sub-sections. The results of GEE criteria This section determines 10 GEE criteria using the Fuzzy AHP method. Table 7 provides the final prioritizing order of GEE criteria. It can be seen that the Socio-Economic Development Policies (A1) is the most relevant GEE criteria with a weight of 0.161 (16.10%) to achieve sustainable environmental regulation and economic efficiency goals. The Green Industrial Development (C3) obtained the second highest weight of 0.124 (12.40%). The Green Growth Agenda (B2) received third importance with a weight of 0.120 (12%) from the overall 10 GEE criteria. The Blue-Green Infrastructure Development (H8), Pollution Control and Waste Management (I9), and Labor Policies (J10) are considered to be the least significant GEE criteria. Overall, the results present that all these criteria are vital for adopting sustainable environmental regulation and GEE criteria in China. The fuzzy pairwise comparison matrix of the AHP method for GEE criteria and sub-criteria are provided in Appendix 1  Tables 11, 12 , 13, 14, 15, 16, 17, 18, 19, 20, 21. The results of GEE sub-criteria

In the above section, the GEE criteria were analyzed and ranked using the Fuzzy AHP method. So, in this section, it further obtained the GEE sub-criteria ranking based on each criterion. Figure 2 shows the final prioritizing order of GEE sub-criteria with respect to Socio-Economic Development Policies (A1). According to the findings, the Sustainable Development Initiative (A11) is the priority GEE sub-criteria with a weight of 0.186 (18.60%). The Green Civil Society Initiative(s) (A12) is a second significant GEE sub-criteria with a weight of 0.184 (18.40%). Furthermore, the Gender Mainstreaming (A14) and Sectoral and Regional Development Initiatives (A15) are recognized as the third important GEE sub-criterion with the same weight of 0.171 (17.10%). Ensuring Stakeholder Participation (A13) and Social Inclusion in Green Economy (A16) has obtained the lowest weight of 0.168 (16.80%) and 0.120 (12%). The analysis reveals that all these sub-criteria are very important for developing environmental regulation and GEE in China. Figure 3 shows the weight and ranking of GEE sub-criteria with respect to the Green Growth Agenda (B2). It can be seen that the Simulate Green Investment (B23) is the significant GEE sub-criteria with a weight of 0.219 (21.90%). The Government Investment Incentives (B24) is the subsequent vital GEE sub-criteria with a weight of 0.218 (21.80%). The Inclusive and Collaborative Planning (B21) and Promote Green Growth Patterns (B22) are the third significant GEE sub-criteria with an equal weight of 0.211 (21.10%), respectively. The Sustainable Special Economic Zone Development (B25) obtained the lowest weight of 0.141 (14.10%). It is identified that all these 5 GEE sub-criteria are vital under the B2 category. Figure 4 shows the weight and ranking of GEE sub-criteria under Green Industrial Development (C3). Under this category, the Green Innovation Initiative for Green Industrial Growth (C33) ranked the first crucial GEE sub-criteria with a weight of 0.189 (18.90%). The Green Product Innovation (C31) obtained the second highest importance with a weight of 0.185 (18.50%). The Industrial Specialization (C34) received third vital GEE sub-criteria with a weight of 0.184 (18.40%). The green Craft Innovation (C32), Industrial Diversity (C35), and Industrial Competition (C36) are moderate to least important sub-criterion to attain the environmental regulation and GEE alternative strategies. It is imperative to mention that the analyzed GEE sub-criterion is very significant to implement sustainable environmental regulation and the GEE system in the country. Figure 6 presents the weight and ranking of the GEE subcriterion with respect to Resource Efficiency (E5). It shows that the Minimization of Environmental Risk (E51) obtained the first ranked GEE sub-criteria under the E5 category with a weight of 0.279 (27.90%). Reducing Waste through Industrial Symbiosis (E53) achieved the second highest weight of 0.224 (22.40%). The Sustainable Public Procurement (E52), Reduce Resources and Energy Consumption (E54), and Efficient Land Use (E55) obtained the third, fourth, and fifth ranks, respectively. Figure 10 presents the weight and ranking of GEE subcriteria with respect to the Pollution Control and Waste Management (I9). The results recommend that the Air Pollution Control (I91) is the most significant GEE subcriteria with a weight of 0.300 (30%). Wastewater Management (I92) appeared as the second vital sub-criteria with a weight of 0.264 (26.40%). The Shared and Circular Economy Promotion (I94) and Solid Waste Management (I93) recognized as third and fourth vital GEE cub-criterion with a weight of 0.247 (24.70%) and 0.188 (18.80%). Figure 11 shows the weight and ranking of GEE subcriteria under Labor Policies (J10). The results indicate that Skill Development (J101) is the top-ranked GEE sub-criteria with a weight of 0.277 (27.70%). The SME Development (J103) appeared as the second important sub-criteria with a weight of 0.254 (25.40%). The Green Jobs (J104) and Occupational Safety and Health (J102) have been identified as third and fourth vital GEE sub-criterion with a weight of 0.242 (24.20%) and 0.228 (22.80%). Therefore, it is determined that most of the sub-criteria under the J10 category are very significant for achieving sustainable environmental regulation and GEE goals in the country.

This sub-section finally determines the overall ranking of GEE sub-criteria with respect to the decision-making goal of the study. The overall weight of sub-criteria has been obtained through multiplying the initial weights of sub-criteria to their respective GEE criteria. Table 8 shows the overall weight and ranking of GEE sub-criteria. The findings reveal that Green Energy Initiative (G71) and Energy-Saving Technology Adoption (G72) are the most significant sub-criteria among all 48 GEE sub-criteria. The G71 and G72 sub-criteria reported the highest final weights of 0.0507 (5.07%) and 0.0362 (3.62%). These GEE sub-criteria belong to the Green Energy Production and Consumption Practices (G7). The Sustainable Development Initiative (A11) followed by Green Civil Society Initiative(s) (A12), Gender Mainstreaming (A14), and Sectoral and Regional Development Initiatives (A15) are the next vital GEE subcriterion. These sub-criteria A11, A12, A14, and A15 belong to the Socio-Economic Development Policies (A1) category. The remaining GEE sub-criteria have been prioritized as follows: A13 < B23 < B24 < J101 < G73 < B21 < B22 < J103 < C33 < H81 < C31 < C34 < I91 < J104 < H84 < C32 < J102 < H82 < I92 < F61 < A16 < I94 < F62 < D41 < F63 < C35 < F64 < B25 < H85 < C36 < D43 < E51 < I93 < D42 < F65 < D44 < E53 < E52 < E54 < D45 < E55. Finally, the findings show that all GE sub-criteria are suitable to be applied for the achievement of sustainable environmental regulation and GEE system in China.

In this section, the ranking of 5 GE alternative strategies has been evaluated using the Fuzzy VIKOR methodology. A group analyzed 6 experts to form group decision-making at the end. The fuzzy decision matrix, fuzzy positive and negative ideal values, fuzzy normalized decision matrix, and the fuzzy values S, R, and Q are presented in Appendix 2 of this paper. All criteria of this study were considered as benefit criteria.

In the adoption of the Fuzzy VIKOR approach, the strategy with lowest Q value has been considered as an optimal alternative. The obtained fuzzy values of S, R, and Q are presented in Table 9 . Moreover, the final ranking based on the Q value is shown in Table 10 . The findings of the Fuzzy VIKOR method indicate that resource efficiency and green purchasing strategy (S2) is the most suitable alternative for sustainable development in China. Local production and utilization strategy (S3) is the second important alternative to follow sustainable environmental regulation and GEE in China. The green economic development strategy (S1) is considered as a third vital alternative of the study. The waste stream management strategy (S4) and green infrastructure strategy (S5) are the least significant alternatives. It is identified that all these strategies are very crucial for the sustainable economic, social, and environmental development of the country.

The Fuzzy AHP and Fuzzy VIKOR methods have been applied systematically to determine the environmental regulation and GEE criteria problem in the context of Chinese economy. This process will serve the country to achieve green development phase. Moreover, this research will help the government and decision-makers to select and evaluate the feasible GEE criteria and alternative strategies for the sustainable development of the country.

This research aims to select, assess, and rank the essential GEE criteria, sub-criteria, and alternative strategies for the implementation of sustainable economic, social, and environmental development in China. This decision-making problem comprehensively provides the comprehension to accomplish environmental regulation and GEE in the post-COVID-19 situation. This process has been done using a fuzzy-based multicriteria analysis. In this regard, the fuzzy-based AHP and VIKOR methods have been used to analyze and rank the GEE criteria and alternative strategies. The evaluation process in the real-life example is complicated and uncertain. Thus, this research used the fuzzy set theory to reduce uncertainty, shortcoming, and fuzziness during the decision-making process.

The ranking of GEE criteria and sub-criteria are presented in Figs. 2, 3, 4, 5, 6, 7, 8, 9, and 10 . The global weights of subcriteria are shown in Table 8 . Among 10 GEE criteria, Socio-Economic Development Policies (A1), Green Industrial Development (C3), and Green Growth Agenda (B2) are the most important criteria for the adoption and boosting up of the sustainable environmental regulation and GEE in China. Furthermore, the final ranking of sub-criteria shows that Green Energy (G71), Energy-Saving Technology Adoption (G72), and Sustainable Development Initiative (A11) are the most significant criteria. After obtaining the findings of criteria and sub-criteria using the Fuzzy AHP approach, this research uses the Fuzzy VIKOR method to analyze and rank the various strategies. This method indicates that resource efficiency and green purchasing (S2) is the most suitable strategy for developing environmental regulation and GEE in the country. The local production and utilization (S3) and green economic development (S1) are the second and third vital GEE alternative strategies.

There are various previous researches on the transformation of environmental regulation and GEE. Such as transition to green industrial growth (Feng and Chen 2018) , environmental regulation can decrease the harmful influence of green technological innovation (Liu et al. 2020) , and environmental regulation, industrial innovation, and green development in the context Chinese manufacturing (Yuan and Xiang 2018) . Furthermore, the economic growth, and energy regulation, and environmental degradation for OECD countries (Lorente and Álvarez-Herranz 2016) . However, this research is the very first attempt in the context of China. Moreover, none of the authors has used fuzzy-based multi-criteria analysis to solve this multi-faceted decision-making problem. This study uses Fuzzy AHP and Fuzzy VIKOR methods to determine this decision-making problem. This research is unique in terms of identifying, evaluating, and ranking environmental regulation and GEE criteria, sub-criteria, and strategies. The proposed research framework determined the 10 criteria, 48 sub-criteria, and 5 strategies. To the best of the author's knowledge, none of the studies utilized composed these important criteria for the evaluation process. The proposed fuzzy multi-criteria analysis framework of this research is a very supportive tool for the government, decision-makers, and stakeholders for sustainable economic and environmental development.

The transition to a green economy and the achievement of GEE are the objectives of governments all over the globe. Achievement of GEE in an economy may depend on specifics of an economy's development, patterns of development, the structure of the economy, structure of environmental regulations, and their implementation in the economy. Moreover, the COVID-19 outbreak diverted the consideration of the governments toward an unparalleled worldwide health crisis posed by a novel coronavirus. The Chinese economy is the first country, which is hit by the COVID-19 pandemic, focusing on economic retrieval. In addition to this, multiple sociocultural-economic-environmental-institutional factors may have their insinuations of the eco-efficient and GEE of the economy. Like other economies, the Chinese economy has started focusing on green economic development and GEE to achieve SDGs. The present study is a pioneer attempt to examine and prioritize GEE alternative strategies in the context of environmental regulations. This study is a fuzzy-based multi-criteria decision analysis in the context of environmental regulations and GEE in China. This study assesses and Resource efficiency and green purchasing 0.001 1 S3

Local production and utilization 0.034 2 S4

Waste stream management 0.091 4 S5

Green infrastructure 0.114 prioritizes 5 alternative strategies for GEE in China while identifying 10 criteria and their respective sub-criteria. The mostly used multi-criteria tool of Fuzzy AHP has been employed to assess and prioritize the criteria and sub-criteria. The Fuzzy VIKOR has been used to evaluate and rank the 5 alternative GEE strategies. The developed criteria, subcriteria, and strategies are qualitative; therefore, it is difficult to analyze and obtain accurate results. Thus, fuzzy-based multi-criteria analysis has been utilized to analyze this decision-making problem. The Fuzzy AHP and Fuzzy VIKOR methods allow an expert to use quantitative terms using a TFN scale. The TFNs can help deal with uncertain and subjective data for reducing ambiguity and uncertainty during the evaluation process. The empirical results of Fuzzy AHP show that Socio-Economic Development Policies (A1) are the most vital criteria to initiate green economic development and GEE for the achievement of SDGs in China. The final ranking of sub-criteria shows that Green Energy (G71) is the most significant in overall sub-criteria. The Fuzzy VIKOR approach shows that resource efficiency and green purchasing (S2) is the most crucial strategy in transiting to the green economy in China. It is determined that the proposed fuzzy decision analysis framework is critical in adopting GEE criteria and strategies in the context of China. Therefore, the governments and decision-makers can apply this model to assess and rank the GEE criteria and alternative strategies for sustainable economic, environmental, and social development in the post-COVID-19 scenario. Data Availability All data generated or analyzed during this study are included in this manuscript.

Ethical approval It is certified that all the authors have complied with ethical requirements.

Consent to participate All the authors participated equally in writing the manuscript.

Consent for publication This manuscript is neither submitted anywhere nor under consideration for publication elsewhere.

Competing interests The authors declare no competing interests. 

Shuangliang Yao. Data curation, Shuangliang Yao. Formal analysis, Shuangliang Yao. Investigation, Shuangliang Yao. Methodology, Shuangliang Yao. Validation, Shuangliang Yao. Writing-original draft, Shuangliang Yao. Writingreview and editing

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