Inclusive green growth for sustainable development of cities in China: spatiotemporal differences and influencing factors

Abstract

Inclusive green growth (IGG) based on coordinating the society, economy, and environment is a new way to reach sustainable development. However, there is a lack of relevant research in developing countries. To bridge this gap, based on a comprehensive index that includes economy, social, and environment, this study evaluates the urban inclusive green growth index (IGGI) of 282 in China from 2003 to 2020 and analyzes the spatiotemporal dynamics and regional differences. Moreover, the spatial Durbin model is employed to explore the plausible influencing factors of urban IGGI in China. The main results show an increasing trend of IGGI in Chinese cities and imbalanced spatiotemporal dynamics. Furthermore, the econometric regress results show that upgrade of industrial structure, opening up, human capital, and urban innovation have significant positive impact on urban IGGI, while the administrative capacity of the government and urban industrialization show negative impact on urban IGGI; human capital not only affects the local IGGI but also has significant spatial spillover effects to the surrounding cities. This finding provides new evidence for China to achieve its 2030 sustainable development goals and sheds lights on how policy can be improved to boost IGGI levels and achieve carbon neutrality in 2060.

Appendices

Appendix 1. The calculation process of the urban economic resilience

The process to calculate urban economic resilience in the main manuscript is as follows:

$${Resis}_i^t=\left(\Delta {Y}_i-\Delta E\right)/\left|\Delta E\right|$$

(24)

where ${Re}{sis}_i^t$ denotes the relative urban economic resilience of city i in year t; ∆Y_i represents the actual economic condition (GDP) of the city i; and ∆E means the predicted economy condition of the city, which is based on the overall region’s economic resilience condition.

$$\Delta {Y}_i={Y}_i^t-{Y}_i^{t-k}$$

(25)

$$\Delta E=\left[\left({Y}_r^t-{Y}_r^{t-k}\right)/{Y}_r^{t-k}\right]{Y}_i^{t-k}$$

(26)

where ${Y}_i^t$ and ${Y}_i^{t-k}$ denote the quantitative indicator of city or region i in the year t and the year t − k, while ${Y}_r^t$ and ${Y}_r^{t-k}$ mean the quantitative indicator of economic resilience of overall nation or the region in the year t and the year t − k.

Formulas (1)–(3) can be further merged as

$${Resis}_i^t=\frac{\left({Y}_i^t-{Y}_i^{t-k}\right)/{Y}_i^{t-k}-\left({Y}_r^t-{Y}_r^{t-k}\right)/{Y}_r^{t-k}}{\left|\left({Y}_r^t-{Y}_r^{t-k}\right)/{Y}_r^{t-k}\right|}$$

(27)

To compare all of the cities, we could conduct the centralized treatment for the results as follow:

$${R}_i=\left({Resis}_i^t-\frac{\sum\limits_i^n{Resis}_i^t}{n}\right){\left(-1\right)}^p$$

(28)

where n means the total amount of cities and (−1)^p denotes the coefficient of correction, p = 0.

We could compare the urban economic resilience of each city by R_i, if R_i > 0, means the economic resilience of city i is better than the overall region, while if R_i < 0, means the economic resilience of city i is worse than the overall region.

Appendix 2 The results of robustness check

Table 6 Estimation results of spatial econometric panel models