Monocentricity or polycentricity? The study on the enterprise environmental performance of urban spatial structure

Abstract

Under the promotion of urban planning and land development policies, the urban spatial structure of China is undergoing a transformation towards polycentricity. The improvement of environmental performance during the process of urban spatial transformation is crucial for urban sustainable development. This paper uses high-resolution population grid data from LandScan for the years 2001–2019 to measure the urban spatial structure of Chinese cities. Then we match the Chinese Industrial Enterprise Database with the Chinese Industrial Enterprise Pollution Emission Database for the years 2001–2014. This paper empirically examines the impact and mechanisms of the polycentric spatial structure of Chinese cities on enterprise pollution reduction at the micro-level. The findings indicate that compared to monocentricity, polycentricity is more conducive to improving enterprise environmental performance. Mechanism tests demonstrate that the improvement in enterprise environmental performance resulting from polycentricity can be achieved through three channels: increasing urban population and density, mitigating urban environmental pollution, and promoting enterprise innovation ability. Heterogeneity analysis reveals that polycentricity is more beneficial for emission reduction among small enterprises, young enterprises and enterprises in regions with higher environmental regulations. This paper provides micro-level evidence and policy implications for further strengthening the aggregation effect of polycentricity and improving enterprise environmental performance in the future.

HIGHLIGHTS

We quantify urban spatial structure using Landscan data in China.

Polycentricity is conductive to the enterprise emission reduction of so₂.

Polycentricity reduces enterprise emission by improving population scale and density.

Reliving urban pollution, improving enterprise innovation are also mechanisms.

Polycentricity is common in the southeast, southwest and northeast cities of China.

Keywords:

• Urban spatial structure
• polycentricity
• enterprise environmental performance

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Waste gas, wastewater, and industrial residue are commonly referred to as the three wastes. In 2020, China’s total sulfur dioxide emissions (so₂) amounted to 3.182 million tons. Among them, industrial pollution sources accounted for 2.532 million tons, accounting for 79.57%. The chemical oxygen demand (COD) in China’s water pollutant emissions reached 25.648 million tons. Among them, industrial pollution sources accounted for 0.497 million tons, accounting for 1.93%. China’s industrial residue generation reached 3,675.46 million tons. The data are from the 2021 China Environmental Statistics Yearbook.

2 In June 2018, the Communist Party of China Central Committee and the State Council issued a document on enhancing ecological protection and fighting pollution, which urged the resolute battle to keep the skies blue, waters clear, and lands clean.

3 The definition of environmental performance is derived from the latest version of the International Organization for Standardization (ISO) Environmental Management-Environmental Performance Assessment-Guidelines, updated in 2021. (ISO 14031:2021) (https://www.iso.org/obp/ui/#iso:std:iso:14031:ed-3:v1:en).

4 Because the pollution data of Chinese industrial enterprises is not fully published, the data in this paper can only be updated to 2014 (Shang, Xu, and Zhao 2022). Therefore, in order to match the data of urban spatial structure, we select the data from 2001 to 2014 as a whole.

5 During the sample study period in this paper, the standard of scale enterprises stipulated by the National Bureau of Statistics of China is the sales revenue of more than 5 million. Compared with small-scale enterprises, large-scale enterprises have a more obvious impact on urban spatial structure. Therefore, this paper deleted the data of small enterprises with sales revenue less than 5 million (Brandt, Van Biesebroeck, and Zhang 2012; Dagestani et al. 2023; Shang, Xu, and Zhao 2022).

Additional information

Funding

This work was supported by ‘the National Natural Science Foundation of China’ [Grant No. 72173079; Grant No. 72304084], ‘the Fundamental Research Funds for the Central Universities of China’ [Grant No. JZ2023HGQA0083; Grant No. JZ2023HGTA0210] and ‘the Scientific Research Fund of Anhui University of Finance and Economics’ (Grant No. ACKYC23022).