Assessment of green economic efficiency in China using analytical hierarchical process (AHP).

Global health, as well as worldwide development regimes, was seriously threatened by the COVID-19 pandemic and Delta variant outbreaks. In addition to pledging to adapt to and mitigate climate change, experts, economists, and policymakers expressed their determination to do so. Green growth and sustainable development have become the focus of policymakers and governments. The progress toward green economic efficiency (GEE), which will benefit the economy, society, and environment, continues. In terms of green growth and development, implementing environmental regulations and policies has been one of the most challenging aspects of the process. China, the world's second-largest economy, has begun its journey to GEE. Nonetheless, the green economy faces many challenges. The objective of the study is to use AHP analysis to analyze environmental regulation and GEE in China. Accordingly, the study identified three alternative approaches to achieve GEE by analyzing four criteria and ten sub-criteria in the context of environmental regulations in China. The analytical hierarchy process (AHP) has been used to rank criteria, sub-criteria, and alternative approaches. According to the model, China's best path to GEE is through resource efficiency and green purchasing strategies. This article offers an insightful assessment of sustainable development in the Chinese economy.

Introduction

Following the Paris Conference and the international consensus on Sustainable Development Goals (SDGs) in 2015, the world's economies have been concentrating on developing and implementing adaptation and mitigation plans (Ahmed et al. 2020). However, the fact that economic development and environmental protection are mutually exclusive goals has become a major source of concern, particularly in emerging countries (Liu et al. 2020). Fast expansion in emerging economies necessitated enormous resource inputs, resulting in substantial environmental damage (Miao et al. 2017). Environmental rules must be promulgated and enforced to achieve eco-efficiency and balance the economic value and environmental effects (Liu et al. 2020). Economists, lawmakers, and economic decision-makers believe that greening economies is a new "engine of growth" rather than a "drag on growth" (UNEP 2011).It presents a significant opportunity for policymakers and administrations to create conditions that will encourage the transition to a green economy. It established the green economic initiative (GEI) in 2008 intending to improve “human well-being and social equity while significantly reducing environmental risks and ecological scarcities”. The green economy was identified as “a low-carbon, resource-efficient, and socially inclusive” economy (UNEP 2011). Green economic efficiency is a multi-dimensional and multi-faceted notion. It needs to be looked at from the standpoint of a multi-criteria decision-making framework. Because production capacity is not confined to GTI, it has a much broader reach, encompassing all socioeconomic activities in the economy. Green economic efficiency (GEE) has recently piqued the interest of academics and commentators. Liu et al. (2020) conducted a study in China to determine the causative linkages between environmental regulation, green technological innovation (GTI), and their interaction term on eco-efficiency. The study looks at how GDI affects eco-efficiency under a variety of environmental restrictions to adjust and maximize the significant utility of green technology. Wang et al. (2019) use spatiotemporal evolution and influencing factors to investigate GEE in the Yangtze River Delta (YRD) from an economic-social-resource-environment perspective. According to the findings, the overall spatial pattern is higher efficiency in the west and poorer efficiency in the east. The fraction of the tertiary sector had the greatest direct effect on local GEE and spatial spillover effects on nearby areas. The study did not capture the whole dynamics of environmental regulation and GEE in China, although it was largely focused on environmental regulation, GTI, and eco-efficiency. The threat of climate change was being combated on a global scale. In the pre-COVID-19 era, it was regarded as a global climatic challenge. The COVID-19 pandemic and Delta variant shifted the overall picture, drawing policymakers' and governments' attention to an emerging and unprecedented global health destruction caused by a novel coronavirus. COVID-19 and Delta variant was presented as a development challenge for all countries, emphasizing the importance of a sustainable development perspective. Global value chains, digitization, debt, and climate change are all affected by the epidemic (Oldekop et al. 2020). Efforts have been made to improve the energy landscape and lower pollution levels in the air (Solangi et al. 2018).Economic and societal concerns have arisen as a result of the global oil market crisis and the COVID-19 and Delta variant pandemic, which must be addressed through intelligent policy design (Henry et al. 2020). There is a pressing need to persevere in climate change mitigation and adaptation activities. Although the pandemic has had an impact on the economy's growth and development paths, economic growth focused on green stimulus and a decrease in fossil fuels investment would add to the beneficial outcomes in averting future climate change (Forster et al. 2020).

The Chinese economy, which was the first to be impacted by the COVID-19 and Delta variant pandemic, is concentrating on recovering. The emphasis is on encouraging infrastructure investment in renewable, intercity transportation, and electric vehicle charging stations. In addition, the Chinese government is focused on five work streams in the aftermath of the COVID-19 and Delta variant: food, water, and power, urbanization adoption, circular economy, and energy storage (Thorpe 2020). All of these factors are critical in achieving environmental legislation, GEE criteria, and alternative methods, transitioning to green industrial growth (Feng and Chen 2018), reducing the negative impact of green technical innovation (Liu et al. 2020), and environmental regulation, industrial innovation, and green development in the context of Chinese manufacturing (Yuan and Xiang 2018). Additionally, Lorente and Alvarez-Herranz (2016) examined economic development, energy regulation, and environmental deterioration in OECD nations. However, in terms of establishing, assessing, and rating environmental regulation and GEE criteria, sub-criteria, and methods, this research is unique. The four criteria, ten sub-criteria, and three methods were defined using the suggested study framework. To the best of the author's knowledge, none of the research included in the assessment process included these critical criteria. The proposed framework for this study is a useful tool for the government, decision-makers, and stakeholders to achieve long-term economic and environmental growth.

Objective of the study

The goal of this article is to evaluate and select potential options for achieving GEE in China after COVID-19. This research prioritizes three different solutions based on four criteria and ten sub-criteria. These criteria and sub-criteria for green economic growth and green capital accumulation in the Chinese economy were distilled from an exhaustive literature analysis in environmental regulation and policy formulations. This research is a thorough examination focused on the analytic hierarchy process (AHP). The analytical hierarchy process is used to evaluate and rank the identified criteria and sub-criteria for green economic efficiency (GEE). AHP estimations are used to determine the rankings of each criterion and sub-criteria. The importance of essential criteria and sub-criteria for green economic efficiency is clarified in this ranking. In keeping with its aims, the article is designed as follows. Section two provides the research background on green economic efficiency criteria, sub-criteria, and alternative strategies. Section three presents details about the AHP method, followed by the research methodology in section four. Research results are analyzed in section five and discussed in section six, before presenting the final comments of this study in section seven (Table 1).

Table 1

List of abbreviation

Abbreviation	Explanation
GEI	Green economic initiative
GTI	Green technological innovation
YRD	Yangtze River Delta
UNEP	United Nations Environment Programme
AHP	Analytical hierarchy process
GEE	Green economic efficiency
A1	Resource efficiency and green purchasing strategy
A2	Green infrastructure strategy
A3	Green economic development strategy
C1	Green energy production and consumption
C2	Labor policies and socioeconomic development policies
C3	Green industrial development
C4	Resources efficiency
S11	Green energy initiative
S12	Energy-saving technology adoption
S21	Skill development
S22	SME development
S23	Green jobs
S24	Sustainable development initiative (SDI)
S31	Green product innovation (GPI)
S32	Industrial specialization
S41	Sustainable public procurement (SPP)
S42	Minimization of environmental risk

Literature review

Green economic efficiency criteria, sub-criteria, and alternative strategies

Based on the AHP analysis of environmental regulations for GEE, the current paper examines and prioritizes strategies for GEE in China. For the transition to green industrial growth, environmental regulation is a critical fulcrum (Feng and Chen 2018). The latest study argues that modest environmental regulation can assist decrease the negative impact of green technical advancement (Liu et al. 2020). The primary goal of this research is to find, evaluate, and select potential GEE strategy criteria and sub-criteria in China. This study uncovers a significant insight toward achieving GEE in the post-COVID-19 and Delta variant era. To this end, the authors conducted a comprehensive literature analysis to establish GEE criteria and sub-criteria in China. Green energy production and consumption practices, labor policies and socioeconomic development policies, green industrial development (GID), resource efficiency, and green industrial development (GID) were among the four primary criteria finalized by the study. Table 2 lists the sub-criteria for each criterion, along with a summary.

Table 2

Sub-criteria for each criterion

Code	Criteria	Sub-criteria	Code	Brief description
C1	Green energy production and consumption	Green energy initiative	S11	After the Paris agreement, green energy initiatives have been introduced globally to reduce energy consumption and switch to renewable energy sources (Kul et al., 2020)
		Energy-saving technology adoption	S12	Additionally, energy consumption must be reduced through the adoption of technologies that reduce energy consumption (Yao, 2021)
C2	Labor policies and socioeconomic development policies	Skill development	S21	A sustainable and low-carbon economy requires the investment of skills in labor policies (ILO 2011)
		SME development	S22	The labor-intensive nature of SMEs and their environmentally friendly nature (ILO, 2011) can contribute toward sustaining green jobs through the development of SMEs (Ali, 2013)
		Green jobs	S23	Development based on sustainable principles incorporates economic growth, maintaining environmental quality, and improving health, justice, and employment for all (Pociovălişteanu et al. 2015)
		Sustainable development initiative (SDI)	S24	Green development and GEE can be fostered by SDI policy regimes (Hou et al. 2019)
C3	Green industrial development	Green product innovation (GPI)	S31	As per (Feng and Chen, 2018), a Green Product Innovation is a design, development, and production process that meets environmental requirements throughout the life cycle of a product
		Industrial specialization	S32	Industrial specialization promotes economic growth (Ma et al., 2019)
C4	Resources efficiency	Sustainable public procurement (SPP)	S41	The SPP is a driver for resource efficiency (Green Growth 2018)
		Minimization of environmental risk	S42	An important objective of the government is to reduce environmental risks. Businesses, industries, and other segments of the economy must minimize environmental risks. Multiple policy instruments have been introduced by the Chinese government to manage environmental risks (Weng et al., 2015)

Strategies for green economic efficiency

Three GEE alternatives are identified in this study. Table 3 summarizes the potential tactics and their brief descriptions. Many countries have traditional environmental rules and economic efficiency systems, which have negative environmental consequences such as climate warming, environmental degradation, air pollution, and human health (such as COVID-19 and Delta Variant). In this context, it is critical to turn traditional financial and environmental optimizations into a system that is both environmentally and economically sustainable. A complete literature survey was conducted to determine the most applicable GEE criteria, sub-criteria, and methodologies in this research. In the case of China, four GEE criteria, ten sub-criteria, and three key methods have been established. This study evaluated this decision-making problem using a multi-criteria decision model, i.e., AHP. This is the first investigation to use an analytic hierarchy process (AHP) model to assess GEE criteria, sub-criteria, and approaches. This study would help the government and decision-makers in putting environmental regulations in place, as well as GEE in achieving green development in China. In the post-COVID-19 and Delta variant scenario, this would also help to strengthen the Chinese economy.

Table 3

GEE alternative strategies

Code	Alternatives	Brief description
A1	Resource efficiency and green purchasing strategy	Production supply strategies are key to green economic development. Sustainable consumption is reflected in resource efficiency and green purchasing. These strategies help communities tap into their purchasing power, energy, resources, water, and green purchasing power (ICMA 2019)
A2	Green infrastructure strategy	Another alternative to achieve GEE is to develop green infrastructure (John et al. 2019). The use of comprehensive and inclusive land-use policies can significantly contribute to the efficient use of resources, the maintenance of environmental quality, and the production of economic activity. Furthermore, local governments, when planning and implementing green initiatives can have a positive impact on the green economy. In addition to improving the business environment (ICMA 2019), local governments can also improve a resident's quality of life
A3	Green economic development strategy	To industrialize, economies are implementing smart and innovation-driven strategies (Cao et al. 2019). The primary objective of economic development is growth. As in traditional economic development, the green economy employs strategies to improve environmental outcomes not just for growth but also for development (ICMA 2019)

Research methodology

Decision analysis based on multiple criteria

The goal of this research is to identify, assess, and rank GEE criteria, sub-criteria, and alternative solutions from the perspective of the Chinese economy. The multi-criteria decision analysis, i.e., AHP approaches, was employed to tackle this multi-faceted decision-making challenge in this case. The AHP is one of the most widely used and appropriate techniques for MCDM. The suggested AHP model and hierarchy structure are shown in Figs. 1, 2. The sub-sections provide AHP strategies for assessing the proposed decision-making problem.

Fig. 1

Proposed AHP model

Fig. 2

Hierarchy structure

Analytic hierarchy process (AHP)

The 'AHP method' is an analytical and prioritization technique described by Saaty in the 1970s (Saaty 1990; Wang et al. 2020). It describes four hierarchical levels: goal, criteria, sub-criteria, and alternatives (Solangi et al. 2019b).

The key steps in this process are,

The first step structures the complex decision problem in a hierarchy. The goal at the top level, criteria at the second level, sub-criteria at the third level, and alternative at the bottom.

The second step developed a pairwise comparison matrix for criteria and sub-criteria with accurate consistency. Let us assume that the matrix X is generated for pairwise comparison, is the number of criteria, then the matrix X will be a matrix where each entry a of the matrix describes the row column of the criterion. We also have a reciprocal value for each diagonal. If a is the element of the row column of the matrix, then for the lower diagonal, we use the formula

For each comparison, the matrix calculates the maximum eigenvalue, consistency index (CI), consistency ratio (CR), and normalized eigenvector to get priority weights for each criteria/ alternative.

Assimilate judgments over numerous levels of hierarchy to produce a complete priority ranking for alternatives. Since the pairwise comparison can be very subjective, AHP uses a consistency check of comparisons.

Eq. (1) shows the consistency index, and Eq. (2) shows that the consistency ratio is calculated respectively.where λ max is the maximum eigen value of the comparison matrix, and n denotes the total number of elements in the comparison.where RI is the random consistency index and which is shown in Table 4. The RI scale of the current study is proposed by Saaty, 1987.

Table 4

Random index

n	1	2	3	4	5	6	7	8	9	10
RI	0.0	0.0	0.58	0.90	1.12	1.24	1.32	1.41	1.45	1.49

A consistency test supports the evaluation of judgments made in a pairwise comparison. In the circumstance of a failure, then the pairwise comparisons are complete again. A consistency ratio less than 0.1 is recommended, and it's standard, while a value more than 0.1 indicates inconsistencies in such a situation, the analytical hierarchy process might not produce significant results.

Data collection

GEE criteria assessment is one of the hardest jobs. Inexperienced individuals often assign weights that are useless to make decisions (Solangi et al. 2019b).Therefore, five professionals who have expertise in relevant field were consulted. These experts include academic researchers, researchers from research institutes, economists, and ecologists. The proposed model was used to analyze the collected data to identify the most suitable GEE strategy within the context of China. Thus, the findings of this study could help in identifying the best GEE criteria, sub-criteria, and strategy.

Results

In the first part, the analytical hierarchy process has been used to analyze and rank GEE criteria, 10 sub-criteria, and three alternatives. These evaluated GEE criteria and alternatives are considered to be very important for the growth and execution of the country's environmental regulations and GEE. Figure 3 comes up with the final priority of the GEE criteria. Therefore, it can be seen from the average results that green energy production and consumption are the most relevant criteria, with a weight of 0.344 (34.40%). Labor policies and socioeconomic development policies ranked second with a weight of 0.238 (23.80%), resource efficiency ranked third with a weight of 0.221 (22.10%), and green industry development ranked fourth with a weight of 0.196 (19.60%).

Fig. 3

Criteria weight

Overall, the results indicate that all these criteria are essential for the adoption of sustainable environmental regulation and GEE criteria in China. The pairwise comparison matrix of the AHP method of the GEE criteria and the sub-criteria are shown in Tables 6, 7, 8, 9, 10 of Appendix 1.

Table 6

Comparison of the relative importance concerning C1

Table 7

Comparison of the relative importance concerning C2

Table 8

Comparison of the relative importance concerning C3

Table 9

Comparison of the relative importance concerning C4

Table 10

Comparison of the relative importance concerning goal: green economic efficiency

In the second part, the weight of the sub-criteria is evaluated. Figure 4 and Table 5 show the global weight of sub-criteria. The results indicate that green energy initiative is the most suitable sub-criteria and ranked first. The remaining GEE sub-criteria have been prioritized as follows: S12 < S42 < S31 < S41 < S32 < S23 < S22 < S24 < S21.

Fig. 4

The relative weight of sub-criteria concerning the goal

Table 5

Global weight of sub-criteria

Criteria	Weight	Rank	Sub-criteria	Weight	Overall weight	Rank
C1	0.344	1st	S11	0.526	0.181	1st
			S12	0.474	0.163	2nd
C2	0.238	2nd	S21	0.221	0.053	10th
			S22	0.257	0.061	8th
			S23	0.291	0.069	7th
			S24	0.231	0.055	9th
C3	0.196	4th	S31	0.608	0.119	4th
			S32	0.392	0.077	6th
C4	0.221	3rd	S41	0.429	0.095	5th
			S42	0.571	0.126	3rd

In the third part, the ranking of the GEE alternatives were evaluated using the analytic hierarchy process. Figure 5 shows the overall result of prioritizing China’s green economy efficiency. The results of the analytic hierarchy process show that the resource efficiency and green purchasing strategy (A1) with a weight of 36.7% is the most suitable alternative for China's sustainable development. The green infrastructure strategy (A2), with a weight of 32.2%, is China’s second important choice to comply with sustainable environmental regulations and GEE. The green economic development strategy (A3) with a weight of 31.0% is considered the third important option in this study.

Fig. 5

Overall priority of alternatives corresponding to all criteria

When evaluating all the alternatives proposed in the criteria used in the ranking, we found that alternatives related to green economic efficiency are the least popular in terms of green industrial development and resource efficiency. In contrast, this alternative is most suitable for the production and consumption of green energy. In terms of green energy production and consumption criteria, resource efficiency and green purchasing are the most popular. In terms of green infrastructure strategy, labor policy and socioeconomic development policy and green industrial development are the most unpopular criteria, as shown in Fig. 6.

Fig. 6

The weighted contribution of the criteria for each alternative

Sensitivity performance of the criteria

In green energy production and consumption criteria, alternative “resource efficiency and green purchase strategy” has a high value and becomes 1st and second position is alternative “green economic development strategy” and alternative “green infrastructure strategy” in the bottom. In labor policies and socioeconomic development policies criteria, alternative “resource efficiency and green are purchasing strategy” and “green economic development strategy” leads while the second position is alternative “green infrastructure strategy”. If we talk about the green industrial criteria, alternative “resource efficiency and green purchase strategy” leads to second-placed alternative “green infrastructure strategy” and “alternative green economic development strategy” the last. In resources efficiency criteria, alternative “green infrastructure strategy” leads over alternative “resource efficiency and green are purchasing strategy” with a very close number while alternative “green economic development strategy” is at third place. The overall result shows that alternative “resource efficiency and green are purchasing strategy” is the best option among the available alternatives. Figure 7 shows the performance sensitivity of the criteria concerning alternatives.

Fig. 7

Performance sensitivity of the criteria concerning alternatives

Discussion

The evaluation process in the real-life example is complicated and uncertain. Thus, this research used the AHP technique to reduce uncertainty, shortcoming. In the post-COVID-19 Delta variant situation, this decision-making problem provides thorough comprehension of environmental regulation and GEE. The present process was carried out through the application of AHP analysis in this regard. Therefore, GEE criteria and alternative strategies have been ranked using the AHP methodology. As in a real-life example, the decision-making process in the evaluation process is complex and uncertain. In this research, AHP has been utilized to reduce uncertainty and shortcoming in the evaluation process. After a comprehensive assessment of the proposed model shows that, among the 4 GEE criteria green energy production and consumption are the most relevant, follow labor policies and socioeconomic development policies, while resource efficiency and green industrial development ranked fourth. Given alternatives, for developing environmental regulation and GEE in the country, resource efficiency and green purchasing (S2) is the best strategy. There have been several prior studies on the evolution of environmental regulation and GEE.

Conclusion

Authorities all across the world are working to make the transition to a green economy and attain GEE. The characteristics of an economy's development, patterns of development, the structure of the economy, the form of environmental regulations, and their execution in the economy may all play a role in achieving GEE. Furthermore, the COVID-19 epidemic distracted authorities' attention away from an unprecedented global health threat posed by a novel coronavirus. The Chinese economy is the first to be affected by the COVID-19 epidemic, with a focus on economic recovery. Moreover, a variety of sociocultural, economic, environmental, and institutional issues may have an impact on the economy's eco-efficiency and GEE. To accomplish the SDGs, the Chinese economy, like other economies, has begun to focus on green economic development and GEE. The current research is a first of its kind attempt to investigate and select GEE alternative techniques in the context of environmental restrictions. In the context of environmental legislation and GEE in China, this study is a multi-criteria decision analysis based on AHP. This study evaluates and prioritizes three potential GEE tactics in China, using four criteria and associated sub-criteria. According to AHP's empirical findings, green energy production and consumption (C1) are the most important requirements for launching green economic development and GEE in China to fulfill the SDGs. The AHP method demonstrates that energy efficiency and green purchasing (A1) are the most important strategies in China's transition to a green economy. The recommended AHP decision-making framework has been established to be significant in the adoption of GEE criteria and tactics in China. As a result, in the post-COVID-19 and Delta variation scenario, governments and decision-makers can use this model to evaluate and rank GEE criteria and alternative solutions for sustainable economic, environmental, and social growth.

Limitation and future recommendation

It is not possible to generalize the results of this study because of some limitations. This research was conducted in China, so special consideration should be given to Chinese characteristics. A certain reference can be drawn for developing countries from the Chinese economy's transformation and development model. The current research considered only four criteria and ten sub-criteria, but it will be expanded in the future. In this article author design the specific MCDM model (AHP) for this problem because it’s a multi-criteria decision-making problem and AHP is the most suitable and reliable process to handle this issue. In the future we can use other technique like Fuzzy VIKOR and Fuzzy TOPSIS. This model can also be extended and used in other developing countries and then compare the results in light of present study.