Industrial symbiotic agglomeration and green economic growth: A spatial difference-in-differences approach

Liu, Jun; Yu, Qian; Song, Shunfeng; Duan, Rong-rong

doi:10.1016/j.jclepro.2022.132560

Cited by 25 publications

(7 citation statements)

References 68 publications

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“…Hence, the choice of an appropriate DEA measurement method is crucial. Concerning influencing factors, researchers predominantly employ models like the Tobit model [14] and spatial econometric model [15] to investigate factors such as industrial structure [2] environmental regulation [16], and industrial agglomeration [17,18] in their impact on green economic efficiency. Regarding the spatial-temporal evolution pattern of green economic efficiency, scholars commonly employ exploratory spatial data analysis [19], kernel density estimation [20], Theil index [21], Dagum Gini coefficient [22], and other methods to explore the dynamic distribution and regional disparities.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Spatial-Temporal Evolution and Its Influencing Factors of Cities’ Green Economic Efficiency: A Case Study of Shandong Province, China

Zhang,

Li,

et al. 2024

Pol. J. Environ. Stud.

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Based on panel data from 16 prefecture-level cities in Shandong Province from 2011 to 2020, the paper utilizes the super-efficiency SBM model with undesirable outputs to measure the green economic efficiency of each city. Spatial autocorrelation analysis and the natural breaks method are applied to analyze the spatial-temporal evolution of green economic efficiency. Lastly, a panel Tobit model is used to analyze the factors affecting green economic efficiency. The study's outcomes are as outlined below: (1) The green economy efficiency of the 16 cities in Shandong Province showed an overall increasing trend from 2011 to 2020. However, there is a noticeable disparity in green economic efficiency among cities, with developed cities exhibiting relatively higher levels of efficiency.(2) The proportion of cities with high green economic efficiency steadily increases, and these highefficiency regions gradually cluster around the provincial capital and the eastern coastal areas. While there is heterogeneous clustering of green economic efficiency, the degree of this heterogeneity decreases over time. (3) Social security, economic development, and technological advancement significantly enhance green economic efficiency, whereas the industrial structure noticeably impedes efficiency. Environmental regulations and urbanization levels have a less pronounced impact on efficiency. Drawing from these findings, this chapter presents targeted policy recommendations.

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Section: Introductionmentioning

confidence: 99%

Analysis of Spatial-Temporal Evolution and Its Influencing Factors of Cities’ Green Economic Efficiency: A Case Study of Shandong Province, China

Zhang,

Li,

et al. 2024

Pol. J. Environ. Stud.

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“…Liu et al (2022) demonstrated that NDEP has a positive effect on improving urban green total factor productivity, but overlooked the potential spatial impact of this policy on surrounding areas [20]. Furthermore, Liu et al (2022) and Zhang and Wei (2023) dissected how NDEP promotes the green economy directly or indirectly using the spatial DID (SDID) model [7,21]. and proved that NDEP reduces pollution and carbon emission and improves CEE of the pilot cities and their neighboring cities [22,23].…”

Section: Introductionmentioning

confidence: 99%

Does Industrial Symbiosis Improve Carbon Emission Efficiency? Evidence from Chinese National Demonstration Eco-Industrial Parks

Ji,

Shao,

Wang

2024

Sustainability

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Improving carbon emission efficiency (CEE) is a necessary consideration in response to economic downturn and climate change. In this case, industrial symbiosis paves the way for cities to conserve energy, reduce carbon emissions, and upgrade economic development patterns. This paper verifies the influence of industrial symbiosis policies on CEE, represented by National Demonstration Eco-industrial Parks (NDEPs). The difference-in-differences (DID) and spatial DID (SDID) models, as well as panel data of 282 Chinese cities during 2003–2019, were used to complete this argumentation process. The results show that NDEP policy dramatically increases the CEE of pilot cities. Compared with cities without NDEP, pilot cities climb by 3.49% on CEE, mainly due to industrial structure upgrading and green technological innovation. Moreover, eastern, non-resource-based, and multi-NDEP cities experience a noticeable increase in CEE. NDEP increases not only the CEE of pilot cities but also that of their neighboring cities within 450 km through diffusion and demonstration effects. All these findings help promote China’s NDEP construction and offer decision-making guidance for climate governance and low-carbon transition in China and others following a similar pathway.

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“…As a new form of industrial organization, there is still a lack of research on how the intermediary variable affects the opening of high-speed rail and the growth of the green economy [19]. With the acceleration of the industrialization process, environmental pollution problems are also spread all over China.…”

Section: Introductionmentioning

confidence: 99%

High-Speed Railway Opening, Industrial Symbiotic Agglomeration and Green Sustainable Development—Empirical Evidence from China

Chen,

Zhu,

Zhao

2024

Sustainability

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In recent years, China’s transportation infrastructure has undergone significant changes. High-speed rail, as a new and favored mode of transportation, offers travelers convenience, efficiency, and punctuality, replacing many high-pollution transportation methods. Based on the characteristics of high-speed rail, this paper selects data from 30 provinces spanning from 1999 to 2019. It utilizes the double-difference method to evaluate the impact of high-speed rail opening on economic sustainability. It has been found that the introduction of high-speed rail contributes to economic sustainability during the examination period. The mechanism test also reveals that the opening of high-speed rail promotes green and sustainable development through the industrial agglomeration effect. In addition, the opening of high-speed rail (HSR) has a significant time lag effect and a spatial spillover effect on green sustainable development. The conclusion complements the theoretical framework regarding the impact of high-speed rail on the environment and regional economic sustainability. It also provides guidance for the efficient utilization of high-speed rail, which holds both theoretical and practical importance.

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Industrial symbiotic agglomeration and green economic growth: A spatial difference-in-differences approach

Cited by 25 publications

References 68 publications

Analysis of Spatial-Temporal Evolution and Its Influencing Factors of Cities’ Green Economic Efficiency: A Case Study of Shandong Province, China

Analysis of Spatial-Temporal Evolution and Its Influencing Factors of Cities’ Green Economic Efficiency: A Case Study of Shandong Province, China

Does Industrial Symbiosis Improve Carbon Emission Efficiency? Evidence from Chinese National Demonstration Eco-Industrial Parks

High-Speed Railway Opening, Industrial Symbiotic Agglomeration and Green Sustainable Development—Empirical Evidence from China

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