نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناس ارشد توسعه اقتصادی و برنامه‌ریزی، دانشگاه سیستان و بلوچستان، زاهدان، ایران

2 استاد اقتصاد، دانشگاه سیستان بلوچستان،زاهدان، ایران

3 پسا دکتری اقتصاد، دانشگاه فردوسی، مشهد، ایران.

چکیده

آلودگی هوا به‌عنوان یکی از مسائل مهم و دغدغه‌های جوامع بشری امروزی معرفی شده است. تأثیر آن بر اقتصاد و سلامت انسان‌ها بسیار مهم و ضروری است. تحقیقات اپیدمیولوژیک نشان می‌دهد که آلاینده‌های هوا می‌توانند منجر به بیماری‌های قلبی و عروقی و در نهایت سکته‌های قلبی شود. پژوهش حاضر بر این اصل تمرکز دارد که استفاده از منابع انرژی تجدیدپذیر می‌تواند به بهبود کیفیت هوا و مرگ‌ومیر ناشی از آلودگی هوا کمک کند. در این پژوهش از روش رگرسیون کوانتایل برای داده‌ها یک‌کشورهای عضو پیمان RCEP در بازه زمانی 2018 تا 1996 استفاده شده است. نتایج نشان می‌دهد که در همه دهک‌های کوانتایل انرژی تجدیدپذیر معنادار و منفی شده که در نهایت می‌توان نتیجه گرفت استفاده از انرژی تجدیدپذیر در مدیریت آلاینده‌های هوا به کاهش مرگ‌ومیر و بهبود کیفیت هوا کمک می‌کند. نتایج همچنین نشان می‌دهد که افزایش تولید ناخالص داخلی می‌تواند منجر به کاهش مرگ‌ومیر ناشی از آلودگی هوا شود؛ درحالی‌که انتشار دی‌اکسیدکربن CO2 و نرخ شهرنشینی مرگ‌ومیر ناشی از آلودگی هوا را افزایش می‌دهد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Heterogeneous Effects of Renewable Energy on Air Pollution-Related Mortality

نویسندگان [English]

  • Fatemeh Rastehmoghadam 1
  • Mohammad Nabi Shahiki Tash 2
  • Emad Kazemzadeh 3

1 Department of Economics, University of Sistan and Baluchestan, Zahedan, Iran.

2 Professor of Economics/ University of Sistan and Baluchestan

3 Department of Economics, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.

چکیده [English]

Air pollution has emerged as a critical concern for contemporary human societies due to its significant implications for both economic stability and public health. This research investigated the potential of renewable energy adoption to enhance air quality and mitigate mortality rates associated with air pollution. Employing the quantile regression method, the study analyzed data spanning from 1996 to 2018 across the member nations of the Regional Comprehensive Economic Partnership. The findings indicated a statistically significant and negative correlation between renewable energy consumption and air pollution-induced mortality across all quantile ranges. Consequently, the implementation of renewable energy sources in managing air pollutants was found to contribute to a reduction in mortality and an improvement in air quality. Furthermore, increased gross domestic product was observed to correlate with decreased air pollution-related mortality, while carbon dioxide emissions and the rate of urbanization were associated with a rise in such mortality. These results underscore the potential of promoting renewable energy and managing emissions as effective strategies for bolstering public health and alleviating the adverse effects of air contamination.
Introduction
Rapid industrialization in recent decades has led to high levels of air pollution, causing numerous health problems. Air pollution originates from various sources, including the burning of solid fuels in homes, particulate matter from industry and transportation, and ground-level ozone. The emission of air pollutants and the resulting climate change pose a significant challenge that has garnered much attention. The overuse of fossil fuels for energy production is a primary cause of these pollutants, leading to respiratory and cardiovascular diseases, as well as global warming.
To address these issues, reducing dependence on fossil fuels and adopting renewable energy sources is essential. Renewable energies not only do decrease air pollution but also help preserve the environment. Recent researches indicate that the use of renewable energy can improve human health by reducing pollutants. Economic collaborations, such as the "Regional Comprehensive Economic Partnership" (RCEP) agreement, can also be effective in reducing air pollution. RCEP member countries constitute a large portion of the global economy and population and account for a substantial share of global emissions.
Given these points, the main research question of this study is:
Can the consumption of renewable energy reduce the number of deaths caused by air pollution in RCEP member countries?
This research also examines the impact of economic growth, urbanization rate, and carbon dioxide on deaths caused by air pollution. The main objective is to identify the effect of renewable energy consumption on air pollution-related mortality in RCEP member countries using the quantile regression method over the period from 1990 to 2018. This topic is important due to its direct impact on public health.
Methods and Material
In this research, we employ quantile regression to investigate the conditional quantiles of the dependent variable. Introduced by Koenker and Bassett Jr. in the 1970s, quantile regression is a method in statistics and econometrics used to model and analyze the effects of variables on different quantiles of the response variable's distribution. It focuses on estimating "how much" of the lowest and highest possible values of a dependent variable are influenced by a set of independent variables, rather than just the mean.
In quantile regression, a specific confidence level (τ, a value between 0 and 1) is chosen, and the estimator calculates the desired quantile for the dependent variable. For instance, setting τ to 0.05 allows the estimation of the 0.05th (lower tail) and implicitly the 0.95th (upper tail) quantiles. This method is particularly useful for modeling more complex relationships and the nonlinear impact of independent variables, serving as an alternative to mean regression (like linear regression) and aiding in the analysis of heterogeneously distributed data.
Moment-based Quantile Regression
To analyze the distributional heterogeneity across countries within a panel dataset, we utilize the moment-based quantile regression approach developed by Machado and Silva (2005, 2019). This method estimates different quantiles of the outcome distribution by accounting for unobserved effects across the distribution. Following Fouquau et al. (2021), this approach assumes that the independent variables influence the dependent variable solely through a location shift. Furthermore, it examines the conditional correlation effects of the determinants of air pollution-related mortality at various quantiles.
The moment-based quantile regression model can be summarized as follows:
Qit (τ∣Xit) = (αi + δiq(τ))+yit′β+Zit′γq(τ) (1)
In Equation (1), αi(τ)=αi+δiq(τ) is a scalar coefficient indicating the τ-quantile fixed effects for a country. Since this coefficient is not location-invariant, the distributional impact differs from the classical fixed effect. Moreover, time-invariant characteristics captured by the distributional effect allow other variables to affect the countries under investigation in different ways (Machado & Silva, 2019).
In this study, we use the quantile regression model to examine the effects of gross domestic product (GDP), renewable energy (RE), carbon dioxide (CO2​) emissions, and urbanization rate (UP) on air pollution-related mortality (Y). The model is specified as:
QYit (τ∣ξt , Xit) = αi+ξt+β1τCO2it+β2τ REit+β3τ GDPit+β4τ UPit(2)
Where GDP, CO2, RE, and UP represent economic growth, carbon dioxide emissions, renewable energy consumption, and urbanization rate, respectively, in order to analyze their effects on mortality due to air pollution.
Results and Discussion
The purpose of tables and figures in documents is to enhance your readers’ understanding of the information presented in the document. It is much lucid and efficient if the information is communicated in tables or figures.
Limit the use of borders or lines in a table to those needed for clarity. In general, use a border at the top and bottom of the table, beneath column headings, and above column spanners. You may also use a border to separate a row containing totals or other summary information from other rows in the table.
Do not use vertical borders to separate data and do not use borders around every cell in a table. Use spacing between columns and rows and strict alignment to clarify relations among the elements in a table. Also, add one blank double-spaced line between the table and any text to improve the visual presentation. Note that the Table 12 presents the results of the panel quantile regression. The coefficients for each independent variable across different quantiles (10th, 25th, 50th, 75th, and 90th percentiles) can be interpreted as follows:
Carbon Dioxide (lCO2): The coefficient for carbon dioxide is positive and statistically significant across all quantiles, indicating a positive relationship with mortality. The impact of carbon dioxide on mortality decreases as we move from lower to higher quantiles. For example, a 1% increase in CO2​ leads to a 0.32% increase in mortality at the 10th percentile, while this effect reduces to 0.198% at the 90th percentile. This suggests that the effect of CO2​ emissions on mortality is more pronounced in countries with lower levels of mortality. Overall, the findings suggest that increased carbon dioxide emissions can lead to a higher mortality rate.
Gross Domestic Product (LGDP): The coefficient for GDP is negative and statistically significant across all quantiles, indicating an inverse relationship with mortality. The negative impact of GDP on mortality tends to decrease in magnitude as we move from lower to higher quantiles. This suggests that economic growth is associated with a reduction in mortality rates.
Urbanization Rate (LUP): The coefficient for the urbanization rate is positive and statistically significant across all quantiles, suggesting that a higher urbanization rate is associated with increased mortality. However, the positive impact of urbanization gradually decreases across higher quantiles.
Renewable Energy (LRE): The coefficient for renewable energy consumption is negative and statistically significant across all quantiles, indicating that increased consumption of renewable energy is associated with a decrease in mortality rates. The negative impact of renewable energy tends to increase in magnitude across higher quantiles.
In summary, the results suggest that higher carbon dioxide emissions and urbanization rates are associated with increased mortality, while higher GDP and renewable energy consumption are associated with decreased mortality. The magnitude of these effects varies across different quantiles of the mortality distribution.
 
Table 1. Quantile regression estimation results




Nvariables


90th


75th


50th


25th


10th




lCO2


0.1891***


0.1052***


0.3252***


0.1029***


0.5123***




LGDP


-0.5768***


-0.6749***


-0.0630***


-1.1041***


-1.2321***




LUP


0.9183**


0.5175**


0.2967**


0.2030**


0.9842**




LRE


-0.1932***


-0.0122***


-0.6902***


-0.2491***


-0.9181***




Note that the symbols *** , ** and * indicate significance levels (1%) , (5%) and (10%), respectively.




 
Table 2. Table Title




 


Group


N


Mean


Std. Deviation


Std. Error Mean




Posttest


Factor 1


xx


xx.xx


x.xx


.xx




Factor 2


xx


xx.xx


x.xx


.xx




*Here is a note on the table.
Conclusion
Air pollution is a complex mixture of gases and particulate matter containing organic and inorganic pollutants in the air. This pollution has serious negative effects on human health and can lead to respiratory diseases, heart conditions, and even premature death. Environmental changes and globalization play a significant role in increasing air pollution and can contribute to the spread of diseases and viruses worldwide. Improving air quality and reducing air pollution can occur through the transition to cleaner energy sources, support for renewable energies, and the reduction of pollutant production. These measures can help decrease mortality caused by air pollution.
Given the importance of public health, studies on air pollution and its effects on humans are essential. Serious efforts to reduce air pollution and improve air quality can help protect public health and reduce premature mortality. In this paper, the impact of air pollution on the economy and human health has been investigated.
In this research, using the quantile regression method and analyzing data from the RCEP member countries from 1996 to 2018, certain results have been obtained. The results indicate that in all quantile deciles, the use of renewable energy has had a significant and negative impact on mortality. The research findings suggest that the use of renewable energy sources can facilitate improved air quality and reduced air pollution, consequently lowering mortality due to air pollution.
Furthermore, the research results show that an increase in gross domestic product can lead to a decrease in the mortality rate caused by air pollution. Increased gross domestic product improves economic and social conditions, which can, in turn, facilitate better health and a reduction in mortality rates. However, the emission of carbon dioxide and the rate of urbanization lead to an increase in the mortality rate. Carbon dioxide emissions and an increased rate of urbanization lead to serious problems in societies. Increased carbon dioxide contributes to global warming and climate change, which have negative impacts on public health. An increased rate of urbanization leads to a higher mortality rate in societies. The reasons for this include traffic and road accidents, air pollution, and reduced access to healthcare services.

کلیدواژه‌ها [English]

  • Air Pollution
  • Renewable Energy
  • Human Health
  • Mortality Reduction
  • Quantile
حسینی علی‌آباد، سیدمحمدرضا؛ شیخ لاری، ولی‌اله و رشیدپور، محمدمعین. (۱۳۹۹). بررسی تأثیر استفاده از انرژی‌های تجدیدپذیر در کاهش آلودگی محیط زیست. شباک، ۶ (۱(پیاپی ۵۲))، 131-۱۱۵.
حیی‌زاده، محمود. (۱۳۹۵). آمار و کاربرد آن در مدیریت (جلد ۱). انتشارات دانشگاه پیام نور.
خدادادی، ثریا؛ پهلوانی، مصیب و حسین‌زاده رمضان. (۲۰۲۲). اثر شدت مصرف انرژی بر انتشار دی‌اکسیدکربن در استان‌های ایران: رهیافت اقتصادسنجی فضایی. تحلیل‌های اقتصادی توسعه ایران، ۸ (۲)، 86-۶۷.‎
رادفر، سهیل و پناهی، روزبه. (۲۰۱۹). توسعة یک متدولوژی برای انتخاب چینش توربین‌ها در مزارع جزرومدی. صنعت حمل و نقل دریایی، ۴(۲)، 10-۴.
کاظم‌زاده، ‌عماد؛ کریمی ‌علویجه، ‌نوشین و ابراهیمی‌سالاری، ‌تقی. (۲۰۱۹). اثر حکمرانی بر گسترش دی‌اکسیدکربن در کشورهای عضو G8: رهیافت رگرسیون پانل کوانتایل. اقتصاد و توسعه منطقه‌ای، ۱۸(۲۶)، 195-۱۷۳.
کاظمیان، غلامرضا؛ رسولی، افشین و خزایی، محمدمهدی. (۱۳۹۶). جایگاه انرژی‌های نو و تجدیدپذیر در زیست‌پذیرانه کردن شهرها، مطالعه موردی شهر تهران. فصلنامه علمی و پژوهشی پژوهش و برنامه‌ریزی شهری، (۲۹)، 118-۹۹.
لطفعلی‌پور، محمدرضا و کاظم‌زاده، ‌عماد. (۲۰۱۹). رابطه بین انتشار گاز کربنیک، رشد اقتصادی و شدت آلودگی کشورها در سطوح مختلف توسعه با استفاده از مدل 3GR. .اقتصاد و توسعه منطقه‌ای، ۱۸(۲۶)، 39-۱۹.
رسولی‌نژاد، ای؛ تقی‌زاده حصاری، ف؛ و تقی‌زاده حصاری، ف. (۱۳۹۹). چگونه مرگ‌ومیر تحت تأثیر مصرف سوخت فسیلی، انتشار CO2 و عوامل اقتصادی در منطقه CIS است؟ انرژی‌ها، ۱۳ (۹)، ۲۲۵۵.
دهباشی، م؛ پهلوانی، م. م. (۱۳۹۱). بررسی رابطه بین مصرف انرژی و رشد اقتصادی در کشورهای منتخب عضو اوپک: رویکرد پانل دیتا. فصلنامه پژوهش‌های اقتصادی (دانشگاه تربیت مدرس)، ۱۲(۴۶)، 26-۱.
References
Adeleye, B. N., Olohunlana, A. O., Ibukun, C. O., Soremi, T., & Suleiman, B. (2022). Mortality rate, carbon emissions, renewable energy and per capita income nexus in Sub-Saharan Africa. Plos one, 17(9), e0274447.
Andersson, Ö., & Börjesson, P. (2021). The greenhouse gas emissions of an electrified vehicle combined with renewable fuels: Life cycle assessment and policy implications. Applied Energy, 289, 116621.‏
Aprilianti, I. (2020). Will RCEP be beneficial for Indonesia. Australian National University, available at: http:www. research gate. net، publication, 341803498 Will RCEP be beneficial for Indonesia.‏
Assamoi, G. R., Wang, S., Liu, Y., Gnangoin, T. B. Y., Kassi, D. F., & Edjoukou, A. J. R. (2020). Dynamics between participation in global value chains and carbon dioxide emissions: empirical evidence for selected Asian countries. Environmental Science and Pollution Research, 27, 16496-16506.‏
Chen, X. H., Tee, K., Elnahass, M., & Ahmed, R. (2023). Assessing the environmental impacts of renewable energy sources: A case study on air pollution and carbon emissions in China. Journal of environmental management, 345, 118525.‏
Chen, Y., Li, Y., & Yan, J. (2019). Tracing air pollutant emissions in China: Structural decomposition and GVC accounting. Sustainability, 11(9), 2551.‏
Cheng, L., Li, M., Zhang, Y., & Wang, X. (2021). The nexus between urbanization, air pollution, and public health: Evidence from Chinese cities. Environmental Science and Pollution Research, 28(15), 19000-19015.
Dawley, S., Marshall, N., Pike, A., Pollard, J., & Tomaney, J. (2014). Continuity and evolution in an old industrial region: the labour market dynamics of the rise and fall of Northern Rock. Regional Studies, 48(1), 154-172.‏
Dehbashi, M., & Pahlavani, M. M. (2013). Investigating the Relationship between Energy Consumption and Economic Growth in Selected OPEC Member Countries: A Panel Data Approach. Quarterly Journal of Economic Research (Tarbiat Modares University), 12(46), 1-26. [In Persian]
Dovis, M., & Zaki, C. (2020). Global value chains and local business environments: Which factors really matter in developing countries?. Review of Industrial Organization, 57, 481-513.
Emodi, N. V., Inekwe, J. N., & Zakari, A. (2022). Transport infrastructure, CO2 emissions, mortality, and life expectancy in the Global South. Transport Policy, 128, 243-253.
Espoir, D. K., Sunge, R., & Bannor, F. (2023). Economic growth, renewable and nonrenewable electricity consumption: Fresh evidence from a panel sample of African countries. Energy Nexus, 9, 100165.‏
Gaur, P. (2021). Regional Comprehensive Economic Partnership A Trade Agreement among Equals?. Journal of Asia Pacific Studies, 6(3).‏
Harris, J. M., & Roach, B. (2017). Environmental and natural resource economics: A contemporary approach. Routledge
Hausman, J. A. (1978). Specification tests in econometrics. Econometrica: Journal of the econometric society, 1251-1271.‏
Hayyizadeh, M. (2016). Statistics and its applications in management (Vol. 1). Payame Noor University Press. [In Persian]
Hosseini Aliabad, S. M., Sheikh Lari, V. A., & Rashidpour, M. M. (2020). Investigating the Effect of Using Renewable Energies in Reducing Environmental Pollution. Shabak, 6(1(consecutive 52)), 115-131. [In Persian]
Hua, L., Ran, R., & Ni, Z. (2023). Are the epidemic prevention facilities effective? How cities should choose epidemic prevention facilities: Taking Wuhan as an example. Frontiers in Public Health, 11, 1125301.‏
Jacobson, M. Z. (2008). On the causal link between carbon dioxide and air pollution mortality. Geophysical Research Letters, 35(3).
Jia, Z., Wang, Y., Chen, Y., & Chen, Y. (2022). The role of trade liberalization in promoting regional integration and sustainability: The case of regional comprehensive economic partnership. Plos one, 17(11).‏
Kazemian, R., Rasouli, A., & Khazaei, H. (2017). The Role of New and Renewable Energies in Making Cities Livable: A Case Study of Tehran. Journal of Urban Research and Planning, 8(29), 99-118. [In Persian]
Kazemzadeh, E., Fuinhas, J. A., Salehnia, N., & Osmani, F. (2023). The effect of economic complexity, fertility rate, and information and communication technology on ecological footprint in the emerging economies: A two-step stirpat model and panel quantile regression. Quality & Quantity, 57(1), 737-763.‏
Kazemzadeh, E., Karimi Alavijeh, N., Ebrahimi Salari, T. (2020). The Effect of Governance on Carbon Dioxide Expansion in the G8 Countries: A Panel Quantile Regression Approach. Journal of Economics and Regional Development, 18(26), 173-195. [In Persian]
Kelsey, J. (2022). Opportunities and Challenges for ASEAN and East Asia from the Regional Comprehensive Economic Partnership on E-Commerce. Economic Research Institute for ASEAN and East Asia.‏
Kharecha, P. A., & Hansen, J. E. (2013). Prevented mortality and greenhouse gas emissions from historical and projected nuclear power. Environmental science & technology, 47(9), 4889-4895.‏
Khodadadi, S., Pahlavani, M., & Hosseinzadeh, R. (2022). The Effect of Energy Intensity on Carbon Dioxide Emissions in Iranian Provinces: A Spatial Econometric Approach. Iranian Economic Development Analyses, 8(2), 67-86. [In Persian]
Koengkan, M., Fuinhas, J. A., & Vieira, I. (2020). The Reaction of the Consumption of Fossil Fuels to 0Trade Openness in Latin America & the Caribbean Countries. Revista de Estudos Sociais, 22(45), 142-170.‏
Koengkan, M., Kazemzadeh, E., Fuinhas, J. A., & Tash, M. N. S. (2023). Heterogeneous impact of eco-innovation on premature deaths resulting from indoor and outdoor air pollution: Empirical evidence from EU29 countries. Environmental Science and Pollution Research, 30(1), 2298-2314.‏
Kuznets, S. (1955). Economic Growth and Income Inequality. The American Economic Review, 45(1), 1-28.
Latif, Y., Shunqi, G., Fareed, Z., Ali, S., & Bashir, M. A. (2023). Do financial development and energy efficiency ensure green environment? Evidence from economies. Economic research-Ekonomska istraživanja, 36(1), 51-72.
Liu, H., Cui, W., & Zhang, M. (2022). Exploring the causal relationship between urbanization and air pollution: Evidence from China. Sustainable Cities and Society, 80, 103783.
Lotfalipour, M., Kazemzadeh, E. (2020). The relationship between carbon dioxide emissions, economic growth and pollution intensity of countries at different levels of development using the 3GR model. Journal of Economics and Regional Development, 18(26), 19-39. [In Persian]
Machado, J. A., & Silva, J. S. (2019). Quantiles via moments. Journal of Econometrics, 213(1), 145-173.‏
Majeed, M. T., Luni, T., & Zaka, G. (2021). Renewable energy consumption and health outcomes: Evidence from global panel data analysis. Pakistan Journal of Commerce and Social Sciences (PJCSS), 15(1), 58-93.
Manisalidis, I., Stavropoulou, E., Stavropoulos, A., & Bezirtzoglou, E. (2020). Environmental and health impacts of air pollution: a review. Frontiers in public health, 8, 14.
Meng, B., Peters, G. P., Wang, Z., & Li, M. (2018). Tracing CO2 emissions in global value chains. Energy Economics, 73, 24-42.‏
Morina, F., Hysa, E., Ergün, U., Panait, M., & Voica, M. C. (2020). The effect of exchange rate volatility on economic growth: Case of the CEE countries. Journal of Risk and Financial Management, 13(8), 177.
Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. Available at SSRN 572504.‏
Radfar, S., Panahi, F., & Rouzbeh. (2019). Developing a methodology for selecting turbine arrangements in tidal farms. Journal of marine transportation industry, 4(2), 4-10. [In Persian]
Rasoulinezhad, E., Taghizadeh-Hesari, F., & Taghizadeh-Hesari, F. (2020). How is mortality affected by fossil fuel consumption, CO2 emissions and economic factors in the CIS region? Energies, 13(9), 2255. [In Persian]
Rasoulinezhad, E., Taghizadeh-Hesary, F., & Taghizadeh-Hesary, F. (2020). How is mortality affected by fossil fuel consumption, CO2 emissions and economic factors in CIS region?. Energies, 13(9), 2255.
Razzaq, A., & Al-Mudimigh, A. (2023). Comparative Analysis of ICT Readiness in RCEP Member Countries. Regional Comprehensive Economic Partnership, 44.‏
Shah, M. H., Salem, S., Ahmed, B., Ullah, I., Rehman, A., Zeeshan, M., & Fareed, Z. (2022). Nexus between foreign direct investment inflow, renewable energy consumption, ambient air pollution, and human mortality: a public health perspective from non-linear ARDL approach. Frontiers in public health, 9, 814208.
Shahbaz, M., & Sinha, A. (2019). Environmental Kuznets curve for CO2 emissions: a literature survey. Journal of Economic Studies, 46(1),
106-168.‏
Shahbaz, M., Bhattacharya, M., & Ahmed, K. (2015). Growth-globalisation-emissions nexus: the role of population in Australia (pp. 1-33). Monash Univ., Department of Economics.‏
Shahzad, U., Fareed, Z., Shahzad, F., & Shahzad, K. (2021). Investigating the nexus between economic complexity, energy consumption and ecological footprint for the United States: New insights from quantile methods. Journal of Cleaner Production, 279, 123806.‏
Shapiro, J. S., & Walker, R. (2018). Why is pollution from US manufacturing declining? The roles of environmental regulation, productivity, and trade. American Economic Review, 108(12), 3814-3854.‏
Shapiro, S. S., & Francia, R. S. (1972). An approximate analysis of variance test for normality. Journal of the American statistical Association, 67(337), 215-216.‏
Shimizu, K. (2021). The ASEAN Economic Community and the RCEP in the world economy. Journal of contemporary East Asia studies, 10(1), 1-23.
Sun, K., Xiao, H., Jia, Z., & Tang, B. (2023). Estimating the effects of regional value chains of the RCEP in a GVC-CGE model. Journal of Asian Economics, 88, 101647.
Tarín-Carrasco, P., Im, U., Geels, C., Palacios-Peña, L., & Jiménez-Guerrero, P. (2022). Reducing future air-pollution-related premature mortality over Europe by mitigating emissions from the energy sector: Assessing an 80% renewable energies scenario. Atmospheric Chemistry and Physics, 22(6), 3945-3965
Tushar, W., Saha, T. K., Yuen, C., Morstyn, T., Poor, H. V., & Bean, R. (2019). Grid influenced peer-to-peer energy trading. IEEE Transactions on Smart Grid, 11(2), 1407-1418.
Wang, H., & Wei, W. (2020). Coordinating technological progress and environmental regulation in CO2 mitigation: The optimal levels for OECD countries & emerging economies. Energy Economics, 87, 104510.‏
Wilson, J. D. (2015). Mega-regional trade deals in the Asia-Pacific: Choosing between the TPP and RCEP ?. Journal of contemporary asia, 45(2), 345-353.
Wu, S., Wei, T., Qu, Y., Xue, R., Wang, H., & Shan, Y. (2023). How does global value chain embeddedness affect environmental pollution? Evidence from Chinese enterprises. Journal of Cleaner Production, 140232.‏
Wurlod, J. D., & Noailly, J. (2018). The impact of green innovation on energy intensity: An empirical analysis for 14 industrial sectors in OECD countries. Energy Economics, 71, 47-61.‏
You, W., Zhang, Y., & Lee, C. C. (2022). The dynamic impact of economic growth and economic complexity on CO2 emissions: An advanced panel data estimation. Economic Analysis and Policy, 73, 112-128.‏
Zhang, X., Han, L., Wei, H., Tan, X., Zhou, W., Li, W., & Qian, Y. (2022). Linking urbanization and air quality together: A review and a perspective on the future sustainable urban development. Journal of Cleaner Production, 346, 130988.
‏Zhao, C., & Wang, B. (2022). How does new-type urbanization affect air pollution? Empirical evidence based on spatial spillover effect and spatial Durbin model. Environment International, 165, 107304.‏
Zhou, W., Zhu, B., Chen, D., Griffy-Brown, C., Ma, Y., & Fei, W. (2012). Energy consumption patterns in the process of China’s urbanization. Population and Environment, 33, 202-220.‏