Energy consumption and pollution control from the perspective of industrial economic activity: An empirical study of China’s coastal provinces

From the perspective of production performance, energy supply are the basic material conditions. However, greenhouse gas, air pollution and waste water are also produced in the process of production. If the undesired characteristics are ignored in the process of performance evaluation, the production efficiency will be misestimated. Based on this, this study uses Data Envelopment Analysis (DEA) to evaluate the undesired output, and discusses the production efficiency with thermal consumption in Chinese port cities, especially with severe shipping emissions, during 2015–2019. The empirical results show that the efficiency declines first (2015–2017) and then increases (2018–2019) when considering the undesired output of wastewater and SO₂ generated by thermal consumption.

Introduction

Since China’s power supply is mainly fossil fuel power generation, since 2014, China has vigorously promoted the implementation of Ultra-Low Emissions and Energy Conversation Projects in its coastal power plants in order to effectively reduce environmental pollution (Wang and Li, 2019; Zhang et al., 2021a; Lei Zhang et al., 2022). Stricter standards for energy efficiency and environmental protection have been introduced, and the emission concentration of air pollutants from newly built generating units should basically meet the emission limits for gas turbine units, with the focus on developing clean combustion technologies (Han et al., 2018; Zhang et al., 2021b; Yansong Zhang et al., 2022). For example, the application of pressurized fluidized bed combined cycle (PFBC) power generation technology, integrated coal gasification combined cycle (IGCC) power generation technology and high efficiency ultra-supercritical (USC) coal-fired power generation technology and coupling power generation technology based on coal generators, etc. (Qian et al., 2021; Wei et al., 2021). And other different types of fuel oil units in service will implement ultra-low emission transformation, such as adding desulfurization, denitration and dust removal devices and other technical means on the original unit to reduce the discharge of industrial pollutants such as wastewater and sulfur dioxide. But some scholars mentioned that thermal production caused by the undesirable output (e.g., shipping emission) in the port city is still prominent (Rezaei et al., 2019; Gregoris et al., 2021; Chen et al., 2022).

Sulfur dioxide and nitrogen oxide emissions from ship fuel burning have been major air pollutants in port cities. The most commonly used marine fuels include light diesel oil, heavy diesel oil, fuel oil and residual fuel oil. Specific include: 0# diesel, −10# diesel, 20# heavy wood, 4# fuel oil, 120# fuel oil, 180# fuel oil, 380# fuel oil (Kondrasheva et al., 2018; Fang et al., 2021; Jing Chen et al., 2021). Depending on the tonnage of the ship (the size and type of engine used), different ships need different marine fuels. In Shanghai, for example, sulfur dioxide and nitrogen oxide emissions from ships accounted for 12.4% and 11.6% of the local total emissions in 2012, while the figures rose to 25.7% and 29.4% in 2015, respectively, according to the Shanghai Environmental Monitoring Center. Data from Hong Kong’s Environmental Protection Department showed that in 2016, ships accounted for 49% of sulfur dioxide, 37% of nitrogen oxide, 38% of PM10 and 44% of PM2.5 emissions (Chen et al., 2019; Ma et al., 2022; Yang et al., 2022). Therefore, it is necessary to evaluate the undesirable output of energy consumption in coastal area.

In this study, the power supply is analyzed from the perspective of electricity and heating supply, and an economic model is used to discuss. Different from the previous traditional efficiency model, energy consumption activities in coastal areas are economic behaviors that have both positive impacts on economic production and negative impacts on environmental conservation. If the output value of pollution generated by energy consumption activities is not taken into account in the analysis process, there will be an error in the estimation. Based on the above discussion, this paper therefore studies the situation of energy consumption and environmental pollution in coastal cities. From the perspective of undesired output, this paper discusses the production efficiency of cities in Fujian, Zhejiang and Jiangsu provinces of China. On the basis of estimating output efficiency, Malmquist Productivity Index is further applied to discuss the correlation between output and productivity.

Literature review

Models such as Tobit model, spatial Durbin model, GMM panel estimation and multiple regression analysis are often used to evaluate the impact mechanism of industrial environmental efficiency (Emrouznejad and Yang, 2016; Yang and Li, 2017; Young and Lipták, 2018; Zhang et al., 2020; Sunari Magar et al., 2021). For example, Wang et al. (2019), Hanafiah et al. (2017), Qiu et al. (2022a) used Tobit model to conduct regression analysis and explore the relationship between economic growth, energy consumption and industrial environmental sustainability. Qiu et al. (2022b), Quan et al. (2022), Miao et al. (2020) studies the impact of energy consumption and environmental pollution on technological innovation efficiency of industrial enterprises by using GMM model based on panel data of industrial enterprises in 30 provinces of China. Scholars such as Zhang et al. (2021c), Khan et al. (2019) and Zia et al. (2021) used spatial Durbin model and multiple regression analysis method to study the influence mechanism of industrial transfer and environmental regulation factors on industrial energy and ecological efficiency of provinces in China. Wang and Luo (2022), Cheng et al. (2019) used the prefecture-level data of urban agglomerations in Yangtze River Delta from 2003 to 2016 and the spatial Tobit model to consider the spatial spillover effect. Further regression analysis showed that industrial structure, environmental regulation and innovation level were positively correlated with industrial technology costs. However, foreign direct investment is not conducive to the growth of industrial technology costs (Teng et al., 2021; Wang et al., 2022; Wu et al., 2021). The popularity of these studies leads to more studies on the measurement of industrial environmental efficiency. The research mainly involves the construction of index system and the selection of measure model (Yin et al., 2021; Lirong Yin et al., 2022). Some studies take capital investment and consumption of various resources as input indexes and gross economic output value as output indexes to construct the index system of industrial resource and environmental efficiency (Alvarado et al., 2018). For example, Shahbaz et al. (2021) selects total energy investment and energy resource utilization as input indicators. Scholars such as Zakari et al. (2021) and choose energy consumption, capital and labor input as the input index of industrial sectors, and total industrial output value as the output index of industrial sectors, so as to study the energy efficiency of China’s industrial sectors. Importantly, most studies add industrial pollutant emissions as undesired output, making its efficiency measurement more scientific and reasonable. For example, Emrouznejad et al. (2019) analyzed China’s manufacturing industry by using carbon emission reduction as an unintended output, so as to maintain the productivity index and reduce carbon emissions at the same time. Han et al. (2021) not only adds industrial waste water and SO₂, but also adds industrial soot and industrial waste emissions as undesired output, so as to calculate the value of China’s regional industrial ecological efficiency. However, these current studies generally focus on the overall national situation and rarely make classification judgment for regions, and seldom study the situation of cities with ports as the leading industry. Because in theory, the situation in coastal port cities could be more important because of the presence of high-polluting industries such as shipping.

As for the estimation of industrial resource and environmental efficiency, many scholars currently use ecological footprint method, Stochastic Frontier Analysis (SFA), life cycle method and decoupling model. For example, Cao et al. (2022), Bibi et al. (2022), Chishti et al. (2021), Dagar et al. (2022), Chandel et al. (2022) used the SFA model to explore the energy efficiency of industrial sectors in India north. However, the method of Data Envelopment Analysis (DEA) has increasingly been adopted to measure the efficiency of industrial resources and environment. The early experts and scholars mostly measure the efficiency based on CCR and BCC models. In 1978, Charnes, Cooper and Rhode put forward the first DEA model—CCR model. Then Banker, Charnes and Cooper put forward another basic form of DEA model—BCC model. DEA model does not need any subjective enactment of significant and small cost, nor does it need to set production function in advance, and it can simultaneously measure the efficiency of multiple decision-making units of the same type. Scholars such as Godil et al. (2022), Islam et al. (2021), Zeng et al. (2019), Yang and Zhu (2022) used the traditional CCR-BCC model to calculate the energy efficiency of five Central Asian countries and the Yangtze River Economic Belt respectively. Currently, the improved DEA-SBM model or super-efficiency DEA model and other methods are often used to measure. Muhammad and Khan (2022), Oryani et al. (2021), Oryani et al. (2022), Qin et al. (2021), Rehman et al. (2022), Yufeng Chen et al. (2021) calculates the industrial energy efficiency and industrial environmental efficiency values of 30 provinces and regions in China in different years by constructing the SBM model. Ni Ni Yin et al. (2022) evaluated the industrial energy efficiency of areas along the “Belt and Road” in China by using the SBM model containing unintended outputs. For example, Yang et al. (2020) took undesired outputs in shipping industry into consideration and conducted an empirical analysis of 25 provinces using simple linear programming. In this study, when the linear programming method is used to measure efficiency, the purpose of considering the undesirable output is to get closer to the real efficiency. Moreover, Cecchini et al. (2018), Zakari et al. (2021), Zhang et al. (2021d), Zia et al. (2021), Weimin et al. (2022) used data envelopment analysis (DEA) model to discuss the efficiency including undesired outputs. After the inclusion of undesired outputs, the efficiency value obtained will be lower than the efficiency value estimated in the traditional way, indicating that if the phenomenon of undesired outputs is ignored in the empirical estimation, the efficiency evaluation will be misestimated. Then it affects the judgment of input and production behavior strategy of the object. Gao et al. (2021) measured the green total factor energy efficiency and the effects of urban agglomerations in the Yangtze River Delta. The empirical results show that, under environmental regulation, excessive pollutant emissions led to a decrease in the growth rate of energy and technical efficiency. They believe that the ignorance of the sustainable technology improvement led to the decrease of the growth rate of energy technology progress. And the cumulative growth rate of technical efficiency, GDP, exhaust emissions per unit GDP, and energy consumption have different degrees of influences on energy efficiency. Zhao et al. (2020) used the DEA method and spatial spillover methods to show the infrastructure level and industrial efficiency cities in Pearl River Delta region of China. The results show that regional infrastructure has significant promotion effect and spatial spillover effect on industrial efficiency. However, there are significant differences in the effects of different infrastructures among the three regions. Overall, there are many literatures on environmental efficiency at present but the research on environmental efficiency in coastal areas is relatively rare. In this paper, DEA method is used to reveal the efficiency difference between coastal areas in combination with the practical situation of China’s coastal areas, providing reference for improving the environmental efficiency of China.

Materials and Methods

Data Envelopment Analysis (DEA) mainly constructs a nonparametric broken line surface and boundary from observed data in the way of linear programming. The relative efficiency of each required estimation sample compared with this boundary is calculated accordingly. Traditionally, the observed data are constructed into a nonparametric broken line surface and boundary by linear programming method, and the relative efficiency between samples is compared. In the production activity category, there are I producers, and each producer has n inputs to carry out the production activity and produce the final output M. For the ith producer, these output and input data can be represented by vector qi and xi respectively. Where N×I and M×I represent input matrix X and output matrix Q respectively. DEA makes mathematical planning for the comparison of all outputs and inputs of each producer by using the productivity ratio pattern. Therefore, the estimation model is the vector form expressed in the following Eq. 1:

$\begin{array}{c}{\max }_{\text{θ},\text{λ}}\text{u}\prime {\text{q}}_{\text{i}}/\text{v}\prime {\text{q}}_{\text{i}}\\ \text{s}\text{.t}\text{.}\\ \text{u}\prime {\text{q}}_{\text{j}}/\text{v}\prime {\text{q}}_{\text{j}}\le 1\\ \text{u},\text{v}\ge 0\\ \text{j}=\mathrm{1,2},\dots \dots \dots .,\text{I}\end{array}(1)$

Through linear programming of Eq. 1, all producers are solved to find the most favorable weight for each producer’s input distribution. By introducing Eq. 1 into the dual pattern, the output guiding pattern of Eq. 2 can be obtained:

$\begin{array}{c}{\text{Max}}_{\text{θ},\text{λ}}\text{θ}\\ \text{s}\text{.t}\text{.}\\ {\text{θq}}_{\text{j}}\le \text{Qλ}\\ {\text{θx}}_{\text{i}}\ge \text{Xλ}\\ \text{λ}\in {\text{R}}^{+}\end{array}(2)$

Eq. 2 is the most commonly used type of datagram analysis, where θ is a scalar. λ is a constant vector with dimension I × 1. The θ value obtained is the production efficiency of the i-th producer, and the θ value is between 0 and 1. If it is 1, it represents the point on the boundary. The producer is defined as an efficient operator. According to Eq. 2, the number of samples to be studied (for example, there are I samples) is determined to solve the production efficiency values respectively.

Considering the existence of undesirable outputs in reality, if the estimation equation is still estimated in accordance with Eq. 2, it is possible to misestimate the efficiency result of undesirable outputs. Therefore, we need to distinguish output Q from desired output Q and from undesired output B. In order to distinguish it from $\lambda$ of Eq. 2, we assume that the constant vector $\vartheta$, the desired output vector $O_Q$, and the undesired output value $O_B$ are taken into account. Where, $O_Q+O_B=Y$ indicates that the total output is the sum vector of desired output value and undesired output value. Therefore, Eq. 2 is extended to Eq. 3 as follows:

$\begin{array}{c}\mathit{Max}\mathit{Scores}\\ \text{s}.\text{t}\text{X}\vartheta +Scores{g}_x\le x_k\\ \text{Q}\vartheta -Scores{O}_Q\ge y_k,\\ \text{B}\vartheta -Scores{O}_B\ge b_k,\vartheta \ge 0\end{array}(3)$

Where, the production may be set as:

$\text{S}=\left\{\left(x,y\right)|x\ge \text{X}\vartheta ,\text{y}\le \text{Q}\vartheta ,\text{b}\ge \text{B}\vartheta \right\}(4)$

Eq. 4 represents that in the case of given desired and undesired output value, the production behavior must weigh the proportion of the two to find the optimal efficiency result. Finally, it can be concluded through linear programming:

$\begin{array}{c}\text{Max}\text{θ}\\ \text{s}\text{.t}\text{.}\\ \text{θQ}\le \text{Q}\vartheta \\ \text{θB}=\text{B}\vartheta \\ {\text{x}}_{\text{j}}\ge \text{X}\vartheta \\ \vartheta \in {\text{R}}^{+}\end{array}(5)$

Eq. 5 is the relative ratio of efficiency values in each period. When the value is less than 1, it means that productivity increases relative to the previous period; when the value is greater than 1, it means that productivity decreases relative to the previous period. The empirical data in this paper are extracted from the Statistical Yearbook of Chinese Cities regularly published by the Department of Urban Social and Economic Survey of the National Bureau of Statistics from 2015 to 2020, which includes the statistical data of urban construction and other aspects of 656 cities (including prefecture-level and above cities and county-level cities) in China. In the present research, we used the data of 33 coastal cities in Fujian, Zhejiang and Jiangsu provinces revealed by the yearbook from 2015 to 2019. In the selection of variables, the economic activity variables were taken as the output variables of urban GDP (Unit: million RMB), PM2.5 (annual average concentration of fine suspended particulates in 2.5 microns) and sulfur dioxide (Unit: ton), among which PM2.5 and sulfur dioxide were the undesired output. Secondly, employees (Unit: 10,000 people), industrial electricity consumption (Unit: 10,000 KWH) and fixed asset investment (Unit: million RMB, excluding agricultural products) are input variables. In addition, in order to analyze the impact of environmental governance and economic conditions on production efficiency, we also consider urban per capita income and environmental fiscal expenditure. The descriptions of relevant variables are summarized in Table 1.

TABLE 1

TABLE 1. Definition of variables and the descriptive statistics.

Results

According to the estimated results of Eqs 2, 3, we can obtain the production efficiency of each city from 2015 to 2019 considering environmental pollution. Table 2 lists the production efficiency values of each province over the years by year and province. Firstly, it can be found that by province, taking into account the undesirable output of PM2.5 and sulfur dioxide generated in industrial heating activities, Jiangsu Province has the highest production efficiency, followed by Fujian Province, and Zhejiang Province has the lowest. For example, from the perspective of annual changes, the production efficiency of all samples showed a trend of decreasing first (2015–2017) and then increasing (2018 and 2019). The empirical results show that with the evolution of time, the production efficiency of China’s coastal cities over the years has improved as a whole.

TABLE 2

TABLE 2. Production efficiency estimation results.

Table 3 collates productivity changes over the years according to Eq. 4. As can be seen from the figure, productivity pointer less than 1 means productivity declines relative to the previous period, while productivity pointer greater than 1 means productivity increases relative to the previous period. It can be found that the productivity indicator showed a downward trend from 2015 to 2017 and an upward trend from 2018 to 2019. The results in Table 3 are consistent with those in Table 2. Such estimation results show that the production performance of industrial economic activities in cities decreases first and then rises, taking into account the output value generated by electric heating supply and pollution problems.

TABLE 3

TABLE 3. Productivity changes.

Figure 1 further depicts the changes of each region in Table 3. It can be found that, although Jiangsu province has the best performance in production efficiency on the whole, from the perspective of productivity change, Fujian Province has the best productivity change on the whole, showing an increase since 2017. One possible reason for the downward trend and then upward trend is that China has been vigorously promoting the implementation of ultra-low emission and energy saving renovation projects in domestic power generation since 2014. In our opinion, this reflects that the adjustment of the thermal policy made local industries first face the pain period and the stage of transformation of production mode, and the efficiency declined during 2015–2017. After the completion of the transformation stage, the overall efficiency and productivity gradually improved. This proves that the promotion of policy has improved the efficiency value of production input, and this change has achieved the result of curbing environmental pollution while pursuing production capacity.

FIGURE 1

FIGURE 1. Productivity changes over years.

Conclusion

Through considering undesired output, this paper explores the production efficiency of 33 Chinese coastal cities dominated by coastal industry and shipbuilding industry from 2014 to 2019 considering the undesired output. Considering the undesired output of air pollution generated in industrial energy consumption activities, Fujian Province has the highest production efficiency, followed by Jiangsu Province, and Zhejiang Province has the lowest. From the perspective of annual change, the production efficiency of coastal cities showed a changing trend of decreasing first (2015–2017) and then increasing (2018–2019), reflecting the thermal production regulation and the input level has reached a certain balance, thus driving the increase of efficiency.

According to the policies of the National Energy Administration of China, the state has formulated a number of supporting policies, such as electricity price, power generation and sewage charge, in order to mobilize the enthusiasm for the renovation of shipping industry and coastal industry. At present, China pays 0.5 cent for electricity and 1 cent for heat for new and active units that achieve ultra-low emission levels. Where the pollutant discharge concentration is more than 50% below the limits set by the State or local authorities, the policy of halving the pollutant discharge fee shall be earnestly implemented. The empirical results show that the efficiency decreases first and then increases, which should be related to the adjustment of China’s electric and heating power policy in recent years.

It can be seen that energy-intensive industries are easy to manage and produce quick results, and they can be channeled through policies such as electricity and heating prices. This paper suggests that in the future, the industries such as shipping should continue to take the lead in carrying out ultra-low emission transformation and greatly reduce the emission of air pollutants, which can not only contribute to the control of air pollution, but also explore ways for other energy-intensive industries to implement ultra-low emission transformation in the future, and help promote the cleaner thermal combustion in China. The empirical results of this paper confirm that the policy promotion has improved the efficiency value of economic production input level on the one hand, and on the other hand, the promotion of environmental protection policy has brought positive effects on economic activities and sustainable environmental development under the attempt to improve the air pollution problem. However, it is needed to point out the limitations of this paper. Since this paper focuses on the situation before COVID-19, future follow-up studies may further include comparative studies before and after the epidemic (Emrouznejad and Yang, 2016; Sunari Magar et al., 2021; Yang and Li, 2017; Young and Lipták, 2018; Zhang et al., 2020).

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author contributions

QL contributed to the study conception and design. Material preparation, data collection, and analysis were performed by QL, and QL commented on previous versions of the manuscript. QL read and approved the final manuscript.

Acknowledgments

This research thanks for the comments from reviewers.

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Alvarado, R., Ponce, P., Criollo, A., Córdova, K., and Khan, M. K. (2018). Environmental degradation and real per capita output: New evidence at the global level grouping countries by income levels. J. Clean. Prod. 189, 13–20. doi:10.1016/j.jclepro.2018.04.064

CrossRef Full Text | Google Scholar

Bibi, M., Khan, M. K., Shujaat, S., Godil, D. I., Sharif, A., and Anser, M. K. (2022). How precious metal and energy resources interact with clean energy stocks? Fresh insight from the novel ARDL technique. Environ. Sci. Pollut. Res. 29 (5), 7424–7437. doi:10.1007/s11356-021-16262-7

CrossRef Full Text | Google Scholar

Cao, H., Khan, M. K., Rehman, A., Dagar, V., Oryani, B., and Tanveer, A. (2022). Impact of globalization, institutional quality, economic growth, electricity and renewable energy consumption on Carbon Dioxide Emission in OECD countries. Environ. Sci. Pollut. Res. 29 (16), 24191–24202. doi:10.1007/s11356-021-17076-3

CrossRef Full Text | Google Scholar

Cecchini, L., Venanzi, S., Pierri, A., and Chiorri, M. (2018). Environmental efficiency analysis and estimation of CO2 abatement costs in dairy cattle farms in umbria (Italy): A SBM-DEA model with undesirable output. J. Clean. Prod. 197, 895–907. doi:10.1016/j.jclepro.2018.06.165

CrossRef Full Text | Google Scholar

Chandel, R. B. S., Khan, A., Li, X., and Xia, X. (2022). Farm-level technical efficiency and its determinants of rice production in indo-gangetic plains: A stochastic frontier model approach. Sustainability 14 (4), 2267. doi:10.3390/su14042267

CrossRef Full Text | Google Scholar

Chen, D., Tian, X., Lang, J., Zhou, Y., Li, Y., Guo, X., et al. (2019). The impact of ship emissions on PM2. 5 and the deposition of nitrogen and sulfur in Yangtze River Delta, China. Sci. Total Environ. 649, 1609–1619. doi:10.1016/j.scitotenv.2018.08.313

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, F., Aqeel, M., Maqsood, M. F., Khalid, N., Irshad, M. K., Ibrahim, M., et al. (2022). Mitigation of lead toxicity in Vigna radiata genotypes by silver nanoparticles. Environ. Pollut. 308, 119606. doi:10.1016/j.envpol.2022.119606

PubMed Abstract | CrossRef Full Text | Google Scholar

Cheng, Y., Shao, T., Lai, H., Shen, M., and Li, Y. (2019). Total-factor eco-efficiency and its influencing factors in the Yangtze River delta urban agglomeration, China. Int. J. Environ. Res. Public Health 16 (20), 3814. doi:10.3390/ijerph16203814

PubMed Abstract | CrossRef Full Text | Google Scholar

Chishti, M. Z., Ahmad, M., Rehman, A., and Khan, M. K. (2021). Mitigations pathways towards sustainable development: Assessing the influence of fiscal and monetary policies on carbon emissions in BRICS economies. J. Clean. Prod. 292, 126035. doi:10.1016/j.jclepro.2021.126035

CrossRef Full Text | Google Scholar

Dagar, V., Ahmed, F., Waheed, F., Bojnec, Š., Khan, M. K., and Shaikh, S. (2022). Testing the pollution haven hypothesis with the role of foreign direct investments and total energy consumption. Energies 15 (11), 4046. doi:10.3390/en15114046

CrossRef Full Text | Google Scholar

Emrouznejad, A., and Yang, G. L. (2016). A framework for measuring global Malmquist–Luenberger productivity index with CO2 emissions on Chinese manufacturing industries. Energy 115, 840–856. doi:10.1016/j.energy.2016.09.032

CrossRef Full Text | Google Scholar

Emrouznejad, A., Yang, G. L., and Amin, G. R. (2019). A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries. J. Operat. Res. Soc. 70 (7), 1079–1090.

CrossRef Full Text | Google Scholar

Fang, X., Wang, Q., Wang, J., Xiang, Y., Wu, Y., and Zhang, Y. (2021). Employing extreme value theory to establish nutrient criteria in bay waters: A case study of xiangshan bay. J. Hydrology 603, 127146. doi:10.1016/j.jhydrol.2021.127146

CrossRef Full Text | Google Scholar

Gao, D., Li, G., and Li, Y. (2021). Government cooperation, market integration, and energy efficiency in urban agglomerations—Based on the quasi-natural experiment of the Yangtze River delta urban economic coordination committee. Energy & Environ., 0958305X211047480. doi:10.1177/0958305X211047480

CrossRef Full Text | Google Scholar

Godil, D. I., Sarwat, S., Khan, M. K., Ashraf, M. S., Sharif, A., and Ozturk, I. (2022). How the price dynamics of energy resources and precious metals interact with conventional and islamic stocks: Fresh insight from dynamic ARDL approach. Resour. Policy 75, 102470. doi:10.1016/j.resourpol.2021.102470

CrossRef Full Text | Google Scholar

Gregoris, E., Morabito, E., Barbaro, E., Feltracco, M., Toscano, G., Merico, E., et al. (2021). Chemical characterization and source apportionment of size-segregated aerosol in the port-city of Venice (Italy). Atmos. Pollut. Res. 12, 261–271. doi:10.1016/j.apr.2020.11.007

CrossRef Full Text | Google Scholar

Han, L., Han, B., Shi, X., Su, B., Lv, X., and Lei, X. (2018). Energy efficiency convergence across countries in the context of China’s Belt and Road initiative. Appl. Energy 213, 112–122. doi:10.1016/j.apenergy.2018.01.030

CrossRef Full Text | Google Scholar

Han, Y., Zhang, F., Huang, L., Peng, K., and Wang, X. (2021). Does industrial upgrading promote eco-efficiency?─ A panel space estimation based on Chinese evidence. Energy Policy 154, 112286. doi:10.1016/j.enpol.2021.112286

CrossRef Full Text | Google Scholar

Hanafiah, M., Abdullah, R., Azmi Murad, M. A., Din, J., and Wahid, N. (2017). Infrastructure requirements for experience based factory model in software development process in A collaborative environment. Acta Inf. malays. 1 (2), 09–10. doi:10.26480/aim.02.2017.09.10

CrossRef Full Text | Google Scholar

Islam, M., Khan, M. K., Tareque, M., Jehan, N., and Dagar, V. (2021). Impact of globalization, foreign direct investment, and energy consumption on CO2 emissions in Bangladesh: Does institutional quality matter? Environ. Sci. Pollut. Res. 28 (35), 48851–48871. doi:10.1007/s11356-021-13441-4

CrossRef Full Text | Google Scholar

Jing Chen, J., Wang, Q., Huang, J., and Chen, X. (2021). Motorcycle ban and traffic safety: Evidence from a quasi-experiment at Zhejiang, China. J. Adv. Transp. 2021, 1–13. doi:10.1155/2021/7552180

CrossRef Full Text | Google Scholar

Khan, M. K., Teng, J. Z., Khan, M. I., and Khan, M. O. (2019). Impact of globalization, economic factors and energy consumption on CO2 emissions in Pakistan. Sci. total Environ. 688, 424–436. doi:10.1016/j.scitotenv.2019.06.065

PubMed Abstract | CrossRef Full Text | Google Scholar

Kondrasheva, N. K., Rudko, V. A., Kondrashev, D. O., Gabdulkhakov, R. R., Derkunskii, I. O., and Konoplin, R. R. (2018). Effect of delayed coking pressure on the yield and quality of middle and heavy distillates used as components of environmentally friendly marine fuels. Energy fuels. 33 (1), 636–644. doi:10.1021/acs.energyfuels.8b03756

CrossRef Full Text | Google Scholar

Lei Zhang, L., Huang, M., Li, M., Lu, S., Yuan, X., and Li, J. (2022). Experimental study on evolution of fracture network and permeability characteristics of bituminous coal under repeated mining effect. Nat. Resour. Res. 31 (1), 463–486. doi:10.1007/s11053-021-09971-w

CrossRef Full Text | Google Scholar

Lirong Yin, L., Wang, L., Huang, W., Tian, J., Liu, S., Yang, B., et al. (2022). Haze grading using the convolutional neural networks. Atmosphere 13 (4), 522. doi:10.3390/atmos13040522

CrossRef Full Text | Google Scholar

Ma, K., Hu, X., Yue, Z., Wang, Y., Yang, J., Zhao, H., et al. (2022). Voltage regulation with electric taxi based on dynamic game strategy. IEEE Trans. Veh. Technol. 71 (3), 2413–2426. doi:10.1109/TVT.2022.3141954

CrossRef Full Text | Google Scholar

Miao, C. L., Meng, X. N., Duan, M. M., and Wu, X. Y. (2020). Energy consumption, environmental pollution, and technological innovation efficiency: Taking industrial enterprises in China as empirical analysis object. Environ. Sci. Pollut. Res. 27 (27), 34147–34157. doi:10.1007/s11356-020-09537-y

CrossRef Full Text | Google Scholar

Muhammad, B., and Khan, M. K. (2022). Do institutional quality and natural resources affect the outward foreign direct investment of G7 countries? J. Knowl. Econ., 1–22. doi:10.1007/s13132-021-00866-y

CrossRef Full Text | Google Scholar

Ni Ni Yin, N. N., Wang, K. L., Yao, Z., Ding, L. L., and Miao, Z. (2022). Total-factor air environmental efficiency and its influencing factors in the areas along the belt and road in China: A spatial spillover perspective. Energy & Environ. 33 (4), 663–695. doi:10.1177/0958305x211015146

CrossRef Full Text | Google Scholar

Oryani, B., Koo, Y., Rezania, S., Shafiee, A., Khan, M. K., and Mahdavian, S. M. (2021). The role of electricity mix and transportation sector in designing a green-growth strategy in Iran. Energy 233, 121178. doi:10.1016/j.energy.2021.121178

CrossRef Full Text | Google Scholar

Oryani, B., Koo, Y., Shafiee, A., Rezania, S., Jung, J., Choi, H., et al. (2022). Heterogeneous preferences for EVs: Evidence from Iran. Renew. Energy 181, 675–691. doi:10.1016/j.renene.2021.09.071

CrossRef Full Text | Google Scholar

Qian, X., Bai, Y., Huang, W., Dai, J., Li, X., and Wang, Y. (2021). Fuzzy technique application in selecting photovoltaic energy and solar thermal energy production in belt and road countries. J. Energy Storage 41, 102865. doi:10.1016/j.est.2021.102865

CrossRef Full Text | Google Scholar

Qin, X., Godil, D. I., Khan, M. K., Sarwat, S., Alam, S., and Janjua, L. (2021). Investigating the effects of COVID-19 and public health expenditure on global supply chain operations: An empirical study. Oper. Manag. Res., 1–13. doi:10.1007/s12063-020-00177-6

CrossRef Full Text | Google Scholar

Qiu, L., He, L., Lu, H., and Liang, D. (2022a). Pumped hydropower storage potential and its contribution to hybrid renewable energy co-development: A case study in the qinghai-tibet plateau. J. energy storage 51, 104447. doi:10.1016/j.est.2022.104447

CrossRef Full Text | Google Scholar

Qiu, L., He, L., Lu, H., and Liang, D. (2022b). Systematic potential analysis on renewable energy centralized co-development at high altitude: A case study in qinghai-tibet plateau. Energy Convers. Manag. 267, 115879. doi:10.1016/j.enconman.2022.115879

CrossRef Full Text | Google Scholar

Quan, Q., Liang, W., Yan, D., and Lei, J. (2022). Influences of joint action of natural and social factors on atmospheric process of hydrological cycle in Inner Mongolia, China. Urban Clim. 41, 101043. doi:10.1016/j.uclim.2021.101043

CrossRef Full Text | Google Scholar

Rehman, A., Ma, H., Khan, M. K., Khan, S. U., Murshed, M., Ahmad, F., et al. (2022). The asymmetric effects of crops productivity, agricultural land utilization, and fertilizer consumption on carbon emissions: Revisiting the carbonization-agricultural activity nexus in Nepal. Environ. Sci. Pollut. Res. 29 (26), 39827–39837. doi:10.1007/s11356-022-18994-6

CrossRef Full Text | Google Scholar

Rezaei, A., Hassani, H., Fard Mousavi, S. B., and Jabbari, N. (2019). Evaluation of heavy metals concentration in jajarm bauxite deposit in Northeast of Iran using environmental pollution indices. Malays. J. Geosci. 3 (1), 12–20. doi:10.26480/mjg.01.2019.12.20

CrossRef Full Text | Google Scholar

Shahbaz, M., Topcu, B. A., Sarıgül, S. S., and Vo, S. V. (2021). The effect of financial development on renewable energy demand: The case of developing countries. Renewable Energy 178, 1370–1380.

CrossRef Full Text | Google Scholar

Sunari Magar, R., Shrestha, P. K., and Kayastha, P. (2021). Manifestation of optimal route alignment selection of rural road using gis and least cost path (lcp) model with engineering and environmental suitability perspective: A case study in Nepal. Malays. J. Geosci. 5 (2), 56–63. doi:10.26480/mjg.02.2021.56.63

CrossRef Full Text | Google Scholar

Teng, J. Z., Khan, M. K., Khan, M. I., Chishti, M. Z., and Khan, M. O. (2021). Effect of foreign direct investment on CO2 emission with the role of globalization, institutional quality with pooled mean group panel ARDL. Environ. Sci. Pollut. Res. 28 (5), 5271–5282. doi:10.1007/s11356-020-10823-y

CrossRef Full Text | Google Scholar

Wang, H., and Luo, Q. (2022). Can a colonial legacy explain the pollution haven hypothesis? A city-level panel analysis. Struct. change Econ. Dyn. 60, 482–495. doi:10.1016/j.strueco.2022.01.004

CrossRef Full Text | Google Scholar

Wang, Y., and Li, J. (2019). Spatial spillover effect of non-fossil fuel power generation on carbon dioxide emissions across China's provinces. Renew. Energy 136, 317–330. doi:10.1016/j.renene.2019.01.012

CrossRef Full Text | Google Scholar

Wang, J., Wang, S., Li, S., Cai, Q., and Gao, S. (2019). Evaluating the energy-environment efficiency and its determinants in Guangdong using a slack-based measure with environmental undesirable outputs and panel data model. Sci. Total Environ. 663, 878–888. doi:10.1016/j.scitotenv.2019.01.413

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, H., Hou, K., Zhao, J., Yu, X., Jia, H., and Mu, Y. (2022). Planning-Oriented resilience assessment and enhancement of integrated electricity-gas system considering multi-type natural disasters. Appl. energy 315, 118824. doi:10.1016/j.apenergy.2022.118824

CrossRef Full Text | Google Scholar

Wei, H., Dai, J., Mahariq, I., Ghasemi, A., Mouldi, A., and Brahmia, A. (2021). Simultaneous synthesis of H2, O2, and N2 via an innovatory energy system in coronavirus pandemic time: Design, techno-economic assessment, and optimization approaches. Int. J. Hydrogen Energy. doi:10.1016/j.ijhydene.2021.12.044

CrossRef Full Text | Google Scholar

Weimin, Z., Sibt-e-Ali, M., Tariq, M., Dagar, V., and Khan, M. K. (2022). Globalization toward environmental sustainability and electricity consumption to environmental degradation: Does EKC inverted U-shaped hypothesis exist between squared economic growth and CO2 emissions in top globalized economies. Environ. Sci. Pollut. Res. Int., 1–11. doi:10.1007/s11356-022-20192-3

CrossRef Full Text | Google Scholar

Wu, X., Liu, Z., Yin, L., Zheng, W., Song, L., Tian, J., et al. (2021). A haze prediction model in chengdu based on LSTM. Atmosphere 12 (11), 1479. doi:10.3390/atmos12111479

CrossRef Full Text | Google Scholar

Yang, D., Geng, H., and Wang, F. (2020). Marine regulations' impact on environmental sustainability in maritime shipping and coastal economic activities. J. Coast. Res. 109 (SI), 79–84.

CrossRef Full Text | Google Scholar

Yang, H., and Zhu, X. (2022). Research on green innovation performance of manufacturing industry and its improvement path in China. Sustainability 14 (13), 8000. doi:10.3390/su14138000

CrossRef Full Text | Google Scholar

Yang, J., Liu, H., Ma, K., Yang, B., and Guerrero, J. M. (2022). An optimization strategy of price and conversion factor considering the coupling of electricity and gas based on three-stage game. IEEE Trans. Autom. Sci. Eng., 1–14. doi:10.1109/TASE.2022.3171446

CrossRef Full Text | Google Scholar

Yang, W., and Li, L. (2017). Efficiency evaluation and policy analysis of industrial wastewater control in China. Energies 10 (8), 1201. doi:10.3390/en10081201

CrossRef Full Text | Google Scholar

Yansong Zhang, Y., Pan, Z., Yang, J., Chen, J., Chen, K., Yan, K., et al. (2022). Study on the suppression mechanism of (NH4)2CO3 and SiC for polyethylene deflagration based on flame propagation and experimental analysis. Powder Technol. 399, 117193. doi:10.1016/j.powtec.2022.117193

CrossRef Full Text | Google Scholar

Yin, L., Wang, L., Huang, W., Liu, S., Yang, B., and Zheng, W. (2021). Spatiotemporal analysis of haze in beijing based on the multi-convolution model. Atmosphere 12 (11), 1408. doi:10.3390/atmos12111408

CrossRef Full Text | Google Scholar

Young, I. G., and Lipták, B. G. (2018). “Biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total oxygen demand (TOD),” in Analytical instrumentation (New York: Routledge), 59–68.

CrossRef Full Text | Google Scholar

Yufeng Chen, Y., Ma, L., and Zhu, Z. (2021). The environmental-adjusted energy efficiency of China’s construction industry: A three-stage undesirable SBM-DEA model. Environ. Sci. Pollut. Res. 28 (41), 58442–58455. doi:10.1007/s11356-021-14728-2

CrossRef Full Text | Google Scholar

Zakari, A., Toplak, J., Ibtissem, M., Dagar, V., and Khan, M. K. (2021). Impact of Nigeria's industrial sector on level of inefficiency for energy consumption: Fisher ideal index decomposition analysis. Heliyon 7 (5), e06952. doi:10.1016/j.heliyon.2021.e06952

PubMed Abstract | CrossRef Full Text | Google Scholar

Zeng, L., Lu, H., Liu, Y., Zhou, Y., and Hu, H. (2019). Analysis of regional differences and influencing factors on China’s carbon emission efficiency in 2005–2015. Energies 12 (16), 3081. doi:10.3390/en12163081

CrossRef Full Text | Google Scholar

Zhang, H., Song, Y., and Zhang, L. (2020). Pollution control in urban China: A multi-level analysis on household and industrial pollution. Sci. Total Environ. 749, 141478. doi:10.1016/j.scitotenv.2020.141478

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, L., Li, J., Xue, J., Zhang, C., and Fang, X. (2021a). Experimental studies on the changing characteristics of the gas flow capacity on bituminous coal in CO2-ECBM and N-2-ECBM. Fuel Guildf. 291, 120115. doi:10.1016/j.fuel.2020.120115

CrossRef Full Text | Google Scholar

Zhang, L., Huang, M., Xue, J., Li, M., and Li, J. (2021b). Repetitive mining stress and pore pressure effects on permeability and pore pressure sensitivity of bituminous coal. Nat. Resour. Res. 30 (6), 4457–4476. doi:10.1007/s11053-021-09902-9

CrossRef Full Text | Google Scholar

Zhang, L., Godil, D. I., Bibi, M., Khan, M. K., Sarwat, S., and Anser, M. K. (2021c). Caring for the environment: How human capital, natural resources, and economic growth interact with environmental degradation in Pakistan? A dynamic ARDL approach. Sci. Total Environ. 774, 145553. doi:10.1016/j.scitotenv.2021.145553

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, Y., Zhang, H., Fu, Y., Wang, L., and Wang, T. (2021d). Effects of industrial agglomeration and environmental regulation on urban ecological efficiency: Evidence from 269 cities in China. Environ. Sci. Pollut. Res. 28 (46), 66389–66408.

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhao, D., Zhen-fu, L., Yu-tao, Z., Xiao, C., and Shan-shan, L. (2020). Measurement and spatial spillover effects of port comprehensive strength: Empirical evidence from China. Transp. Policy 99, 288–298. doi:10.1016/j.tranpol.2020.09.006

CrossRef Full Text | Google Scholar

Zia, S., Noor, M. H., Khan, M. K., Bibi, M., Godil, D. I., Quddoos, M. U., et al. (2021). Striving towards environmental sustainability: How natural resources, human capital, financial development, and economic growth interact with ecological footprint in China. Environ. Sci. Pollut. Res. 28 (37), 52499–52513. doi:10.1007/s11356-021-14342-2

CrossRef Full Text | Google Scholar