تراکم جوامع صنعتی و محیط زیست: شواهدی از کشورهای آسیا

Arnaut, M., & Dada, J. T. (2023). Exploring the nexus between economic complexity, energy consumption and ecological footprint: new insights from the United Arab Emirates. International Journal of Energy Sector Management, 17(6), 1137-1160.‏ https://doi.org/10.1108/IJESM-06-2022-0015

Balsalobre-Lorente, D., dos Santos Parente, C. C., Leitão, N. C., & Cantos-Cantos, J. M. (2023). The influence of economic complexity processes and renewable energy on CO2 emissions of BRICS. What about industry 4.0?. Resources Policy, 82, 103547.‏ https://doi.org/10.1016/j.resourpol.2023.103547

Brülhart, M., & Mathys, N. A. (2008). Sectoral agglomeration economies in a panel of European regions. Regional Science and Urban Economics, 38(4), 348-362.‏

Chang, C. L., & Oxley, L. (2009). Industrial agglomeration, geographic innovation and total factor productivity: The case of Taiwan. Mathematics and Computers in Simulation, 79(9), 2787-2796.‏ https://doi.org/10.1016/j.matcom.2008.09.003

Chen, D., Chen, S., & Jin, H. (2018). Industrial agglomeration and CO2 emissions: Evidence from 187 Chinese prefecture-level cities over 2005–2013. Journal of Cleaner Production, 172, 993-10. https://doi.org/10.1016/j.jclepro.2017.10.068

Chen, Y., Wang, Z., & Zhong, Z. (2019). CO2 emissions, economic growth, renewable and non-renewable energy production and foreign trade in China. Renewable Energy, 131, 208-216. https://doi.org/210.1016/j.renene.2018.1007.1047.

Cheng, Z. (2016). The spatial correlation and interaction between manufacturing agglomeration and environmental pollution. Ecological indicators, 61, 1024-1032.‏ https://doi.org/10.1016/j.ecolind.2015.10.060

Cui, S., Wang, Y., Xu, P., & Li, L. (2024). Ecological welfare performance, industrial agglomeration and technological innovation: an empirical study based on Beijing–Tianjin–Hebei, Yangtze River Delta and Pearl River Delta. Environment, Development and Sustainability, 26(1), 1505-1528.‏

Doğan B, Saboori B, Can M (2019) Does economic complexity matter for environmental degradation? An empirical analysis for different stages of development. Environmental Science and Pollution Research 26(31):  31900-31912. https://doi.org/31910.31007/s11356-31019-06333-31901.

Doğan, B., Driha, O.M., Balsalobre- Lorente, D., & Shahzad, U. (2021). The mitigating effects of economic complexity and renewable energy on carbon emissions in developed countries. Sustainable Development, 29(1), 1-12. doi: 10.1002/sd.2125.

Dogan, B., Madaleno, M., Tiwari, A.K., & Hammoudeh, S. (2020). Impacts of export quality on environmental degradation: does income matter? Environmental Science and Pollution Research, 27, 13735–13772. https://doi.org/10.1007/s11356-019-07371-5.

Dong, F., Yu, B., & Pan, Y. (2019). Examining the synergistic effect of CO2 emissions on PM2. 5 emissions reduction: Evidence from China. Journal of cleaner production, 223, 759-771.‏ https://doi.org/10.1016/j.jclepro.2019.03.152

Dumitrescu, E. I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29, 1450–1460. https://doi.org/10.1016/j.econmod.2012.02.014

Eslami Giski Sakineh, Salimi Far Mostafa, Saifi Ahmad. (2022). Investigating the impact of industrial density on pollution density: spatial econometric approach, Planning and Budgeting Quarterly 1401; 27 (1): 155-176. doi:10.52547/jpbud.27.1.155 (In Persian).

Figge, L., Oebels, K., & Offermans, A. (2017). The effects of globalization on Ecological Footprints: an empirical analysis. Environment, Development and Sustainability, 19(3), 863-876.‏ https://doi.org/10.1007/s10668-016-9769-8

Ghazouani, T., & Maktouf, S. (2024, February). Impact of natural resources, trade openness, and economic growth on CO2 emissions in oil‐exporting countries: A panel autoregressive distributed lag analysis. In Natural Resources Forum. 48 (1), 211-231. https://doi.org/10.1111/1477-8947.12318

He, C.F., Huang, Z.J., Ye, X.Y., 2014. Spatial heterogeneity of economic development and industrial pollution in urban China. Stoch. Environ. Res. Risk Assess. 28 (15), 767-781.10.1007/s00477-013-0736-8

Kazemzadeh, E., Fuinhas, J. A., Koengkan, M., & Osmani, F. (2022). The heterogeneous effect of economic complexity and export quality on the ecological footprint: a two-step club convergence and panel quantile regression approach. Sustainability, 14(18), 11153.‏ https://doi.org/10.3390/su141811153

Koenker, R. (2004). Quantile regression for longitudinal data. J. Multivar. 91, 74–89. https://doi.org/10.1016/j.jmva.2004.05.006.

Koenker, R., & Xiao, Z. (2002). Inference on the quantile regression process. Econometrica, 70(4), 1583-1612.  https://doi.org/1510.1111/1468-0262.00342.

Koenker, R., Bassett, J.G. (1978). Regression quantiles. Econometrica: Journal of the Econometric Society, 33-50. https://doi.org/10.2307/1913643.

Li, H., & Liu, B. (2022). The effect of industrial agglomeration on China’s carbon intensity: Evidence from a dynamic panel model and a mediation effect model. Energy Reports, 8, 96-103.‏ https://doi.org/10.1016/j.egyr.2022.05.070

Li, X., Lai, X., & Zhang, F. (2021). Research on green innovation effect of industrial agglomeration from perspective of environmental regulation: Evidence in China. Journal of Cleaner Production, 288, 125583.‏ https://doi.org/10.1016/j.jclepro.2020.125583

Liu, J., Cheng, Z., & Zhang, H. (2017). Does industrial agglomeration promote the increase of energy efficiency in China?. Journal of Cleaner Production, 164, 30-37.‏ https://doi.org/10.1016/j.jclepro.2017.06.179

Liu, X., Yu, Z., Wei, Q., Wen, G., Duan, X., & You, X. (2013). Modification of polycrystalline nanodiamonds by using periodic magnetic field enhanced hydrogen plasma and the application on nanogrinding of thin film magnetic head. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 416, 9-15.‏ https://doi.org/10.1016/j.colsurfa.2012.10.024

Lv, Z., & Xu, T. (2019). Trade openness, urbanization and CO2 emissions: Dynamic panel data analysis of middle-income countries. The Journal of International Trade & Economic Development, 28(3), 317-330. https://doi.org/310.1080/09638199.09632018.01534878.

Mehnat-Far, Y., Osmani, F., Cheshmi, M., & Argha, L. (2023). Does the development of technology and the expansion of trade reduce the ecological footprint? Case study: evidence from developing countries. Economic Growth and Development Research, 14(53), 110-97. doi: 10.30473/egdr.2023.68695.6758 (In Persian).

Nie, X., Wei, X., Xia, Q., & Zhou, M. (2021). Customers’ purchase intention for hydrogen vehicle and industrial agglomeration: Evidence from Jiangsu Province, China. International Journal of Hydrogen Energy, 46(34), 18011-18019.‏ https://doi.org/10.1016/j.ijhydene.2020.10.055

Ning, L., Wang, F., & Li, J. (2016). Urban innovation, regional externalities of foreign direct investment and industrial agglomeration: Evidence from Chinese cities. Research Policy, 45(4), 830-843.‏ https://doi.org/10.1016/j.respol.2016.01.014

Pata, U.K. (2020). Renewable and non-renewable energy consumption, economic complexity, CO2 emissions, and ecological footprint in the USA: testing the EKC hypothesis with a structural break. Environmental Science and Pollution Research, 1-16. https://doi.org/10.1007/s11356-11020-10446-11353.

Pei, Y., Zhu, Y., Liu, S., & Xie, M. (2021). Industrial agglomeration and environmental pollution: based on the specialized and diversified agglomeration in the Yangtze River Delta. Environment, Development and Sustainability, 23(3), 4061-4085.‏

Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. Cambridge Working Papers. Economics, 1240(1), 1.‏ http://ssrn.com/abstract=572504.

Pesaran, M. H., & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of econometrics, 142(1), 50-93.‏https://doi.org/10.1016/j.jeconom.2007.05.010

Porter, M. E. (1998). Clusters and the new economics of competition. Boston: Harvard Business Review.‏ 76 (6), 77-90.

Porter, M. E. (2008). The five competitive forces that shape strategy. Harvard business review, 86(1), 78.‏

Raza, S. A., & Shah, N. (2018). Testing environmental Kuznets curve hypothesis in G7 countries: the role of renewable energy consumption and trade. Environmental Science and Pollution Research, 25(27), 26965-26977. https://doi.org/26910.21007/s11356-26018-22673-z.

Rizov, M., Oskam, A., & Walsh, P. (2012). Is there a limit to agglomeration? Evidence from productivity of Dutch firms. Regional Science and Urban Economics, 42(4), 595-606.‏ https://doi.org/10.1016/j.regsciurbeco.2012.02.006

Song, Y., Zhu, J., Yue, Q., Zhang, M., & Wang, L. (2023). Industrial agglomeration, technological innovation and air pollution: Empirical evidence from 277 prefecture-level cities in China. Structural Change and Economic Dynamics, 66, 240-252.‏

Su, Y., & Yu, Y. Q. (2020). Spatial agglomeration of new energy industries on the performance of regional pollution control through spatial econometric analysis. Science of the Total Environment, 704, 135261.‏ https://doi.org/10.1016/j.scitotenv.2019.135261

Swann, P., & Prevezer, M. (1996). A comparison of the dynamics of industrial clustering in computing and biotechnology. Research policy, 25(7), 1139-1157.‏ https://doi.org/10.1016/S0048-7333(96)00897-9

Wang, J., & Long, F. (2024). Grain industrial agglomeration and grain green total factor productivity in China: A dynamic spatial durbin econometric analysis. Heliyon.‏

Wang, Y., & Wang, J. (2019). Does industrial agglomeration facilitate environmental performance: new evidence from urban China?. Journal of environmental management, 248, 109244.‏ https://doi.org/10.1016/j.jenvman.2019.07.015

Wang, Y., Chen, L., & Kubota, J. (2016). The relationship between urbanization, energy use and carbon emissions: evidence from a panel of Association of Southeast Asian Nations (ASEAN) countries. Journal of Cleaner Production, 112, 1368-1374. https://doi.org/1310.1016/j.jclepro.2015.1306.1041.

Xiao, Z. Y., & Shen, Z. C. (2019). The temporal and spatial evolution of population & industrial agglomeration and environmental pollution and the relevance analysis. Journal of Arid Land Resources and Environment. 2, 1-8. http://open.oriprobe.com/order.htm?id=55334626&ftext=base

Xu, L., Jiang, T., Lin, P., Shao, J. J., He, C., Zhong, W.,.. & Wang, Z. L. (2018). Coupled triboelectric nanogenerator networks for efficient water wave energy harvesting. ACS nano, 12(2), 1849-1858.‏
https://doi.org/10.1021/acsnano.7b08674

Xu, X. (2023). Identifying the Impact of Industrial Agglomeration on China’s Carbon Emissions Based on the Spatial Econometric Analysis. Journal of Environmental and Public Health.‏1- 17. https://doi.org/10.1155/2023/4354068

Zhang, K., Xu, D., & Li, S. (2019). The impact of environmental regulation on environmental pollution in china: an empirical study based on the synergistic efect of industrial agglomeration. Environmental Science and Pollution Research, 26(25), 25775–25788. https://doi.org/10.1007/s11356-019-05854-z.

Zhang, L., Rong, P., Qin, Y., & Ji, Y. (2018). Does industrial agglomeration mitigate fossil CO2 emissions? An empirical study with spatial panel regression model. Energy Procedia, 152, 731-737.‏ https://doi.org/10.1016/j.egypro.2018.09.237

Zheng, Q., & Lin, B. (2018). Impact of industrial agglomeration on energy efficiency in China’s paper industry. Journal of cleaner production, 184, 1072-1080.‏ https://doi.org/10.1016/j.jclepro.2018.03.016