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Abstract

Since the implementation of the compulsory sorting of domestic waste policy in China, the participation rate of residents is low, which leads to the unsatisfactory result of terminal reduction of domestic waste. Therefore, the problem of domestic waste reduction still needs to rely on source reduction. Based on the panel data of 29 provincial capitals in China from 2009 to 2018, this study conducts a comprehensive threshold effect test on per capita GDP and other influencing factors of domestic waste production, conducts panel threshold regression for the factors with threshold value, and explores the nonlinear relationship between per capita GDP and domestic waste production under the influence of different threshold variables. The results show that when the urban population density is less than 272 people/km2, the increase of 1% of per capita GDP will lead to a decrease of 0.251% in the domestic waste production, otherwise, it will lead to an increase of 0.249%; when the per capita consumption expenditure is less than the threshold value of 10,260 yuan/year, the influence coefficient of per capita GDP is 0.155, which increases to 0.207 above the threshold. When the share of tertiary industry is taken as the threshold variable, the two threshold values are 61% and 71% respectively. Through the analysis of control variables, it has been found that population size and amount of courier per capita have significant positive effects on domestic waste production, while gas permeability and the number of non-governmental organizations have significant negative effects
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Bibliography

  1. Bollen, K.A., Brand, J.E.(2010). A General Panel Model with Random and Fixed Effects: A Structural Equations Approach, Social Forces, 89,1,pp.1-34. DOI:10.1353/sof.2010.0072.
  2. Chen, Q.(2014). Advanced econometrics and Stata applications (Second Edition), Higher Education Press, Beijing 2014. (in Chinese)
  3. Cheng, J.H., Shi, F.Y., Yi, J.H. & Fu, H. (2020). Analysis of the factors that affect the production of municipal solid waste in China, Journal of cleaner production, 259, pp.120808-120808. DOI:10.1016/j.jclepro.2020.120808.
  4. Cui, T.N. & Wang, L.N. (2018). Regional difference analysis on the relationship between urban domestic waste emission and economic growth, Statistics and decision, 34, 20, pp.126-129. DOI:10.13546/j.cnki.tjyjc.2018.20.030. (in Chinese)
  5. Drew, J., Kortt, M. A. & Dollery, B. (2013). Did the big stick work? An empirical assessment of scale economies and the Queensland forced amalgamation program, Local government studies, 42, 1, pp.1-14, DOI:10.1080/03003930.2013.874341.
  6. Du, C.L. & Huang, T.Z. (2019). From government’s dominance to multi-governance: governance dilemma and innovation path of urban solid waste classification. Administrative tribune, 26b, 4, pp.116-121. DOI:10.16637/j.cnki.23-1360/d.2019.04.016. (in Chinese)
  7. Du, M., Shao, Y.S. & An, S. (2019). Domestic waste and economic growth in Beijing—an empirical study based on panel data. Finance theory and teaching, 6, pp.88-93. DOI:10.13298/j.cnki.ftat.2019.06.018. (in Chinese)
  8. Han, Z., Liu, Y., Zhong, M., Shi, G., Li, Q., Zeng, D. & Zhang, Y. (2018). Influencing factors of domestic waste characteristics in rural areas of developing countries. Waste management, 72, pp.45-54. DOI:10.1016/j.wasman.2017.11.039.
  9. He, Y.Q. & Wang, S.S. (2018). Factor flow and industrial structure upgrading: an analysis of the threshold effect of financial agglomeration. Financial and economics, 8, pp.62-67. DOI:10.19622/j.cnki.cn36-1005/f.2018.08.010. (in Chinese)
  10. Hoyos, R.E.D. & Sarafidis, V. (2006). Testing for cross-sectional dependence in panel-data models. The Stata Journal, 6, 4, pp. 482–496. DOI:10.1177/1536867X0600600403.
  11. Huangfu, H.H. & Li, H.Y. (2018). Analysis on influencing factors of municipal solid waste production. Science-technology and management, 20, 4, pp.44-49. DOI:10.16315/j.stm.2018.04.004. (in Chinese)
  12. Jia, D.X. & Huang, J. (2015). Threshold effect, economic growth and carbon emission. Soft science, 29, 4, pp.67-70. DOI:10.13956/j.ss.1001-8409.2015.04.15. (in Chinese)
  13. Jiang, K. (2019). Urban livelihood and green development – hazards and prevention of landfill leachate. Journal of green science and technology, 10, pp.133-134. DOI:10.16663/j.cnki.lskj.2019.10.051. (in Chinese)
  14. Liu S.S. & Dai S.L. (2022). Why is it so difficult to implement the policy of household garbage classification in urban communities? Policy implementation process model analysis. Resources and Environment in Arid Areas, 36, 5, pp.1-7. DOI:10.13448/j.cnki.jalre.2022.112. (in Chinese)
  15. Madden, B., Florin, N., Mohr, S. & Giurco, D. (2019). Using the waste Kuznet's curve to explore regional variation in the decoupling of waste generation and socioeconomic indicators. Resources, Conservation & Recycling, 149, C, pp.674-686. DOI:10.1016/j.resconrec.2019.06.025
  16. Mao, K.Z., Sun, J.J. & Song, C.J. (2018). Has the consumption growth of urban residents exacerbated the domestic pollution. East China economic management, 32, 4, pp.87-95. DOI:10.19629/j.cnki.34-1014/f.170831014. (in Chinese)
  17. Nicolli, F. & Lafolla, V. (2012). Waste dynamics, country heterogeneity and European environmental policy effectiveness. Journal of environmental policy and planning, 14, 4, pp.371-393. DOI:10.1080/1523908X.2012.719694.
  18. Pang, L., Ni, G.C. & Yan, G.X. (2004). Hazards of Municipal Solid Waste and Countermeasures for Comprehensive Prevention and Control of Pollution. Environmental Science Dynamics, 2, pp.15-16. DOI:10.19758/j.cnki.issn1673-288x.2004.02.007. (in Chinese)
  19. Qin, B.T. & Ge, L.M. (2019). The transfer of highly polluting Industries and overall Environmental pollution in China-an empirical study based on the threshold Model of Interregional relative Environmental Regulation. China Environmental Science, 39, 8, pp.3572-3584. DOI:10.19674/j.cnki.issn1000-6923.20190604.001. (in Chinese)
  20. Ren, X. (2018). Analysis on the threshold effect of economic growth on haze pollution in the Yangtze River Economic Belt. Statistics and decision, 34, 20, pp.138-141. DOI:10.13546/j.cnki.tjyjc.2018.20.033. (in Chinese)
  21. Singh, A. & Raj, P. (2018). Segregation of waste at source reduces the environmental hazards of municipal solid waste in Patna, India. Archives of Environmental Protection, 44, 4, pp.96-110. DOI:10.24425/aep.2018.122306.
  22. Song, G.J. & Dai X.L. (2020). Policy Framework Design of Urban Domestic Waste Management Based on Source Classification and Resource Recovery. Journal of Xinjiang Normal University (Philosophy and Social Sciences Edition), 41, 4, pp.109-125+2. DOI:10.14100/j.cnki.65-1039/g4.20200123.001. (in Chinese)
  23. Wang, C., Li, Q. & Li, L. X. (2020a). Influencing factors and future trend prediction of municipal solid waste—Based on inter provincial zoning. Journal of Beijing Institute of Technology (social sciences edition), 22, 1, pp.49-56. DOI:10.15918/j.jbitss1009-3370.2020.1491. (in Chinese)
  24. Wang, D.D., Jian, L.R. & Fu, S.S. (2020b). Study on incentive and supervision mechanism of classified recycling of urban solid waste. China environmental science, 40, 7, pp.3188-3195. DOI:10.19674/j.cnki.issn1000-6923.2020.0357. (in Chinese)
  25. Wang, X.F., Ma Z.H., Mu Z.M.et al. (2010). Study on multi-factor prediction model of municipal solid waste output based on BP neural network. Anhui Agricultural Sciences, 38, 10, pp.5475-5477. DOI:10.13989/j.cnki.0517-6611.2010.10.167. (in Chinese)
  26. Xu, B., Zhao, Y., Ju, M.T. et al. (2019). Regional difference of municipal solid waste generation in China Based on the STIRPAT model. China environmental science, 39, 11, pp.4901-4909. DOI:10.19674/j.cnki.issn1000-6923.2019.0571. (in Chinese).
  27. Xu, L.L., Yan, Z. & Cui, S.H. (2013). Path analysis of influencing factors on municipal solid waste generation: A case study of Xiamen City. China environmental science, 33, 4, pp.1180-1185. DOI:10.13671/j.hjkxxb.2013.04.021. (in Chinese)
  28. Yang, K., Kwan, H.Y., Yu, Z. & Tong, T. (2020). Model selection between the fixed-effects model and the random-effects model in meta-analysis. Statistics and its Interface, 13,4,pp.501-510. DOI: 10.4310/SII.2020.v13.n4.a7.
  29. Yang, X.F., Wang, M.F. & Hu, Q. (2019). Garbage classification: action dilemma, governance logic and policy path. Governance Research, 35, 6, pp.108-114. DOI:10.15944/j.cnki.33-1010/d.2019.06.012. (in Chinese)
  30. Zhao, Y., Ge, X.Q. & Li, X.F. (2016). Analysis on influencing factors of municipal solid waste production. Statistics and decision, 23, pp.91-94. DOI:10.13546/j.cnki.tjyjc.2016.23.023. (in Chinese)
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Authors and Affiliations

Li Yang
1
ORCID: ORCID
Hong-Yan Wang
1
Lan Yi
2
Xiang-Zhen Shi
1
Wei Deng
1

  1. International Business School, Shaanxi Normal University, China
  2. Jinhe Center for Economic Research, Xi’an Jiaotong University, China
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Abstract

Many studies on middle income trap draw attention to the product trapt hat can be expressed as the fact that countries are stuck in the production and export of unsophisticated products. In this sense, it is stated that the role of a country in the production and export of sophisticated goods is one of the determinant factors to increase the level of income. In the literature, the concept of economic complexity, which is expressed as gaining competitiveness of complex products in terms of production and export, is noteworthy in recentyears. In this framework, relationship between the per capita GDP and the economic complexity is examined with regression analysis in this study for selected countries with high-level of income. In the analysis, in which random coefficient panel regression model is applied, a significant relationship was found between the two variables for Austria, Finland, Hong Kong, Japan, Norway,Singapore and Sweden.

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Authors and Affiliations

Semanur Soyyiğit

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