The impact of the new energy demonstration city policy on the green total factor productivity of resource-based cities: empirical evidence from a quasi-natural experiment in China

Abstract

The new energy demonstration city policy (NEDC), an essential economic system aiming to implement innovative development strategies and energy structure transformations in China, is crucial for solving the current plight of resource-based cities. This paper studies the effect of the implementation of the NEDC on the green total factor productivity (GTFP) of resource-based cities in China; understanding this relation is of great significance for the further promotion of the NEDC. For this purpose, the SBM-undesirable model is employed to measure GTFP. The propensity score matching and difference-in-difference (PSM-DID) model and the mediation effect model are also utilized to empirically examine the impact of the NEDC on GTFP across a dataset of 106 resource-based cities between 2005 and 2016. The empirical results indicate that the NEDC can significantly improve the GTFP of resource-based cities. After both counterfactual and common trend tests are performed, the research conclusion of this paper is still robust. Moreover, the NEDC promotes the GTFP of resource-based cities through structural effects, technological innovation effects, and fiscal support effects. Furthermore, the effect of the NEDC on GTFP is also affected by the regions, sizes, and types of the different resource-based cities. This research provides evidence for the ability of the NEDC to improve GTFP and optimize the spatial layout of new energy demonstration cities in resource-based cities.

Keywords:

• New energy demonstration city policy
• Green total factor productivity
• Resource-based cities
• Difference-in-differences
• Energy transition

Disclosure statement

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

Notes

1 Resource-based cities are based on the exploitation and processing of minerals, forests and other natural resources in the region as the primary industries. In 2018, the Chinese government stated that resource-exhausted areas, industrially declining areas, and severely ecologically degraded areas should be guided to actively explore the path of characteristic transformation and development.

2 The purpose of the new energy demonstration city policy is to increase the popularization and application of new energy sources, such as distributed wind power generation, solar thermal energy, geothermal energy, new energy transportation, biomass clean fuel, etc., and establish a long-term energy mechanism that is suitable for sustainable urban development.

3 The list includes Zhangjiakou City in Hebei Province, Changzhi City in Shanxi Province, Tongliao City in the Inner Mongolia Autonomous Region, Nantong City in Jiangsu Province, Turpan New area in the Xinjiang Uygur Autonomous Region, Changchun Economic and Technological Development Zone in Jilin Province, and so on.

4 According to the DID method, two groups of dummy variables were constructed (in the experimental group and the control group), in which the dummy variable of the experimental group represents a new energy demonstration city, while the dummy variable of the control group is a non-new energy demonstration city. In the end, 27 resource-based cities were included in the experimental group and 79 resource-based cities were included in the control group.

5 Specifically, the PSM method is to find city $j$ in the control group such that its observable variables are as similar as possible to those of city $i$ in the experimental group $(X_{i }= X_j)$ (i.e., the individuals with the smallest difference in their individual tendency scores are determined between the experimental group and the control group as the comparison object). Then, the individual characteristics of the new energy demonstration cities are used to match the resource-based cities in the control group with similar characteristics to those of the new energy demonstration cities, such that their individual information can finally be fully utilised. When the NEDC is fully influenced by observable factors, the probabilities of city $i$ and city $j$ becoming a new energy demonstration city are similar. Then, based on the PSM method, the DID method was applied to further control for unobservable factors that do not change over time between the experimental and control groups, ultimately reducing the original gap between the experimental and control groups.

6 We used the National Sustainable Development Plan of Resource-based cities (2013-2020) issued by the Chinese government to obtain a list of 106 resource-based prefecture-level cities.

7 Although the prefecture-level city data can be updated to 2018, some of the variables covered in this paper have more missing values in the original data in 2017 and 2018. After filling in a large number of missing values, it may cause estimation bias to the regression results. For example, the variable of technological innovation is derived from the Report on the Innovation Power of Chinese Cities and Industries prepared by the Center for Industrial Development Research of Fudan University and the First Financial Research Institute (the data are only updated to 2016). In addition, the study in this paper has fewer missing values for the period 2005-2016, so this paper uses the data from 2005-2016 for the regression analysis.

8 K-order nearest neighbour matching is performed by finding the nearest K different groups of individuals regarding their propensity scores; these scores are then arithmetically averaged to obtain the matched individuals.