Could ecological restoration reduce income inequality? An analysis of 290 Chinese prefecture-level cities

Abstract

Ecosystem degradation and the serious wealth gap caused by rapid economic development have become problems that cannot be neglected during the progress of pursuing sustainable development and reducing income inequality in China. To determine whether ecological restoration such as vegetation cover could affect the income gap, we used data for 290 prefecture-level cities in China from 2007 to 2018 and analyzed the effect of ecological restoration on income inequality in China. In addition, we chose the year 2012 as a boundary and performed heterogeneity analysis to permit a detailed comparison of the variation in the effect over time. We found that ecological restoration can reduce income inequality in general, but this effect was not statistically significant until 2012. However, due to some practical obstacles (e.g., employment opportunities, educational attainment, social discrimination), reducing income inequality through ecological restoration will be a time consuming process and requires constant effort from the Chinese government and local managers such as funding green industries, providing more targeted technical training for the poor and social services for the rural migrant workers.

Introduction

Income inequality has become one of the primary causes of social unrest and has slowed the efforts to achieve sustainable development of human society and improve both the national and regional economies. For example, since 1978, when Deng Xiaoping initiated the reform of China's economic system and raised the slogan “let some people get rich first,” China has achieved rapid economic growth, but this growth has been accompanied by China's transformation from an economically egalitarian country to a country with an extreme gap between the rich and the poor (Sutherland and Yao 2011). Although Deng emphasized that the ultimate goal is to eliminate poverty and the polarization of wealth in China and achieve “common prosperity,”1 relative poverty and the huge income gap still exist and restrict the development of the economic level of people living in less developed areas (Zhang 2016). According to the National Bureau of Statistics, China's Gini coefficient has exceeded 0.4 since 1994 and continues to increase. The income gap has become a serious problem that demands prompt solutions in the process for China to achieve common prosperity.

Another serious problem caused by China’s rapid economic development and urbanization is environmental degradation, with approximately 2.6 × 10⁶ km² of degraded land, accounting for 27.5% of China's total land area (Taniguchi and Yamanaka 2017). Ecosystem degradation is a major cause of poverty, and poverty further aggravates ecosystem degradation through feedback known as the “poverty trap,” which can prevent sustainable socioeconomic development in ecologically fragile areas (Cao et al. 2020). From the beginning of the twenty-first century, with sustainable development deeply rooted in people’s hearts, environmental issues have gradually become the focus of attention. To date, a series of historically unprecedented ecological restoration projects have been implemented in China to combat ecosystem degradation, such as the Sloping Land Conversion Program, Natural Forest Conservation Program, and National Parks Program. However, the primary goal of nature reserves is conservation and not poverty alleviation. Thus, conservation policies focus more on biological conservation and ignore the economic impacts on local residents (Wang and Yamamoto 2009). For instance, restrictions on the consumption of natural resources may negatively affect the economies of local communities, where natural resources (e.g., timber and nontimber forest products) contribute greatly to the local economy and the per capita income of local residents is low (Brockington and Wilkie 2015). These negative effects will also widen the income gap between the rich and poor.

In September 2015, the United Nations released the Sustainable Development Goals (SDGs), a results-oriented framework for sustainable development that contains 17 goals about essential needs, objectives, and governance (Unit Nations 2015). It was showed that there are synergies between the SDGs, which implies that advances in one goal could benefit progress in another (Pradhan et al. 2017). For example, previous research revealed that ecosystem restoration may show positive impacts on absolute poverty alleviation (e.g., Andam et al. 2010; Roe et al. 2013; Baird 2014; Clements and Milner-Gulland 2015; Ma and Wen 2016), suggesting that there may be synergies between SDG 1 (No Poverty) and SDG 15 (Life on Land). However, whether ecosystem restoration can promote relative poverty alleviation and achieve common prosperity (i.e., synergies between SDG 10 (Reduced Inequalities) and SDG 15) is not yet clear. For instance, Gao et al. (2020) found a notable decline in poverty incidence in most villages in Lijiang, an internationally famous tourist city located in Yunnan, due to environmental factors such as available water storage and geological hazard risks. However, we found that the per capita GDP of Lijiang was nearly 75% of the average level of Yunnan Province and less than 40% of the per capita GDP in Kunming (the provincial capital of Yunnan) for years (National Bureau of Statistics 2019). Similarly, for Lishui city, honored as the “Zhejiang Green Valley,” per capita GDP was only 66% of the average level of Zhejiang Province and less than 50% of the per capita GDP in Hangzhou (the provincial capital of Zhejiang) until 2018 (National Bureau of Statistics 2019). Although China’s president Xi Jinping put forward the thesis “lucid waters and lush mountains are invaluable assets,” suggesting that the Chinese government would endeavor to foster an efficient pattern of green sustainable development and promote a harmonious coexistence of economic development and environmental protection (Xi 2014), the situation of some undeveloped rural residents was not improved with the construction of “lucid waters and lush mountains.”

At present, the amount of research on the relationship between relative poverty (income gap) and the environment is insufficient, and most of them have focused on air pollution or carbon emissions (e.g., Ma et al. 2020; Wang and Zhang 2021; Zhang et al. 2022a; Huo and Chen 2022). We did not find any research mentioning whether or how ecological restoration, such as vegetation cover, could affect the income gap or common prosperity in China. Our aim is to test whether ecological restoration behavior can help reduce income inequality using econometric methods, provide a new path to alleviate inequality of income, and promote common prosperity not only in China but also with suitable modification to account for unique local conditions, in other developing countries around the world. To provide missing information, we first collected data for 290 prefecture-level cities in China, characterized “common prosperity” as thoroughly as possible, and analyzed the effect of ecological restoration on common prosperity with a fixed-effects model. In addition, we performed heterogeneity analysis to permit a detailed comparison of the variation in the effect over time.

Materials and methods

Dividing the concept of common prosperity

It is challenging to find a simple variable that refers to common prosperity, a complicated political economic term. However, since the way of implementing the policy of achieving common prosperity in China is to encourage some people and some regions to prosper prior to others to bring along the latter's prosperity, in time, to achieve the common prosperity, it is reasonable to divide the meaning of common prosperity into two steps: achieving “prosperity,” which means the growth of the resident income of a relatively undeveloped area in a region (e.g., an undeveloped prefecture-level city in a province), and achieving “common,” which means the elimination of the internal income gap in a region (e.g., urban–rural income gap). In this study, we used the municipal–provincial income gap and urban–rural income gap (as well as the Theil index2 for the robustness test later in the heterogeneity analysis) to represent “prosperity” and “common,” respectively. The descriptions and calculation methods of these two variables are reported in Table 1.

Table 1.

Variables that were selected in this study and their descriptions

Category	Variables	Descriptions
Common prosperity	Municipal–provincial income gap (inc)	Per capita income for city i /per capita income of the province where city i is located
	Urban–rural income gap (gap)	Urban per capita disposable income for city i /per capita net income of rural residents for city i
	Income gap (theil)	Theil index for city i in year t was calculated by the following formula: Tit = Yitu * log(Yitu/Pitu) + Yitr * log(Yitr/Pitr), where Yitu is the ratio of total income of urban residents and total income of all residents; Yitr is the ratio of total income of rural residents and total income of all residents; Pitu is the ratio of total population of urban residents and total population of all residents; Pitr is the ratio of total population of rural residents and total population of all residentsa
Ecological restoration	NDVI (ndvi)	Normalized-difference vegetation index for city i
Control variables	Investment (ainv)	Per capita investment in fixed assets for city i
	Transportation (rden)	Road density for city i (length of roads/regional area)
	Unemployment (unemp)	Unemployment rate for city i
	Urbanization (urb)	Proportion of a city’s residents who live in urban area for city i
	Medical services (adoc)	The number of personnel in medical institutions per 104 persons for city i
	Education (aedus)	Per capita educational expenditure for city i
	Science and technology (ascis)	Per capita scientific and technological expenditure for city i
	Government behavior (agfs)	Per capita government fiscal expenditure for city i

^aAccording to the definition given by National Bureau of Statistics, “urban residents” for city i are those who have lived in urban areas of city i for more than 6 months in year t, and other people in city i are defined as “rural residents.” For more information, please see http://www.stats.gov.cn/english/.

Study area, selection of metrics, and data sources

To explore the relationship between ecological restoration and common prosperity in China more comprehensively, we selected 290 prefecture-level cities in 26 provinces based on the availability of data. Provincial-scale municipalities that were governed directly by the central government (i.e., Beijing, Tianjin, Chongqing, and Shanghai) and some cities in special economic zones (i.e., Shenzhen, Zhuhai, Xiamen, and Shantou) usually receive more government financial support for political reasons, and this bias could affect the objectivity of our research results and conclusions. We therefore excluded these cities from our analysis.

First, we chose the normalized-difference vegetation index (NDVI) to measure ecological restoration because it is the most utilized VI and has been used successfully for many years (e.g., Jin and Wang 2016; Zoungrana et al. 2018; Wang et al. 2020), and it is a good pr» for the actual vegetation cover, especially in arid and semi-arid regions (Ma et al. 2021). Second, according to previous research, many factors could influence the income gap. Therefore, we chose investment (Wang 2006), infrastructure factors such as transportation, medical service, education and scientific development (Chan and Ngok 2011; Yang and Gao 2017; Qazi et al. 2018; Huang et al. 2020; Weng et al. 2021), unemployment (Xue and Zhong 2003), urbanization (Lee et al. 2019; Yuan et al. 2020), and the government's attitude toward solving relative poverty (Lei et al. 2016) as control variables that affect the income gap. Tables 1 and 2 define these indicators and summarize their statistical characteristics.

Table 2.

Descriptive statistics of variables used in econometric models

Variable	Obs	Mean	SD	Min	Max
inc	3480	1.016	0.288	0.5	2.555
gap	3480	2.541	0.608	0.690	5.796
theil	3480	0.095	0.054	0.003	0.37
ndvi	3480	0.445	0.133	0.06	0.71
ainv	3480	45 953.74	74 006.63	521.722	1 373 959
rden	3480	1.414	1.839	0.004	22.922
unemp	3480	65.727	54.883	4.955	550
urb	3480	50.085	15.521	4.5	97.62
adoc	3480	21.748	12.148	2.980	178.827
aedus	3480	2639.848	5201.654	106.67	184 676.3
ascis	3480	192.198	250.393	7.449	4243.978
agfs	3480	9970.711	15 421.36	100.972	258 471.5

All data except NDVI came from the China City Statistical Yearbook, national economic development and statistical bulletins for each city from 2007 to 2018. We chose this period to permit an analysis of changes over time and because this covered the period from immediately before the 2008 financial crisis to the most recent data available on the study variables in all cities. We obtained annual values of the NDVI from satellite remote-sensing data using the 16-day average values from 2007 to 2018 obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) data, at a spatial resolution of 1 km. Images were obtained from mid-August of each year, which represented the period of maximum vegetation growth.

Heterogeneity analysis

As a further analysis, we studied whether the effect of ecological restoration on common prosperity changed over time. We chose the year 2012 as a boundary between periods and divided the samples into two parts: 2007–2011 and 2012–2018. We chose this year because China’s government issued the first national territory development plan since the founding of the People's Republic of China, namely, the Main Functional Area Planning (MFAP), in June 2011. For a long time, the lack of coordination in territorial and spatial planning led to disorder in the regulation of China (Fan, 2013). The purpose of MFAP is to define the main function and direction of development, control the intensity of development, gradually achieve a harmonious coexistence of population, economy, resources and the environment, and become a long-term blueprint to guide territorial space development.3 Therefore, we thought the year of 2012 could be a potential node if the effect of ecological restoration on common prosperity varied over time.

MFAP separated China’s territory into four types: development zones to be optimized, key development zones, limited development zones, and prohibited development zones. The development zones to be optimized possess a relatively developed economy, dense population, high development intensity, and more prominent resource and environment problems. The key development zones possess a certain economic foundation, strong resources and environment carrying capacity, great development potential, and good conditions for gathering population and economy. Because limited development zones and prohibited development zones are mainly nature reserves, tourist attractions and forest parks where data are hard to collect, we chose only development zones to be optimized and key development zones as the objects of study in this part. First, we estimated the effect of ecological restoration on common prosperity (i.e., two kinds of urban–rural income gaps) before and after 2012 for all cities (Table 3). Second, we chose cities affected by two types of MFAP (i.e., development zones to be optimized and key development zones) as the sample and estimated the effect before and after 2012 again (Tables 4 and 5). To guarantee the robustness of our results, we chose the Theil index as the substitute variable of the urban–rural income gap.

Table 3.

Regression coefficients, statistical significance, sample size (N), and goodness of fit (R²) for two-way fixed-effect models (individual and time fixed effect) for ecological restoration and two kinds of urban-rural income gaps before and after 2012 in China. The values in brackets under the coefficients are their cluster robust standard errors. See the Methods section for details. Cons means the regression y-intercept. Significance levels: ***p < 0.001, **p < 0.01, *p < 0.05

Variable	2007–2011 lngap	2012–2018 lngap	2007–2011 lntheil	2012–2018 lntheil
lnndvi	0.104	− 0.357***	0.248	− 0.642***
	(0.09)	(0.05)	(0.22)	(0.13)
lnainv	0.028**	− 0.046***	0.102**	− 0.073***
	(0.01)	(0.01)	(0.05)	(0.02)
lnrden	− 0.008*	0.004	− 0.012	0.010*
	(0.00)	(0.00)	(0.01)	(0.01)
lnunemp	− 0.010	− 0.002	− 0.035*	− 0.006
	(0.01)	(0.00)	(0.02)	(0.01)
lnurb	− 0.073*	− 0.175***	− 0.219*	− 0.969***
	(0.03)	(0.04)	(0.10)	(0.16)
lnadoc	− 0.012	− 0.027**	− 0.019	− 0.059**
	(0.01)	(0.01)	(0.03)	(0.03)
lnaedus	− 0.024	− 0.044**	- 0.064	− 0.096**
	(0.02)	(0.01)	(0.05)	(0.04)
lnascis	− 0.045**	0.004	− 0.066*	− 0.010
	(0.01)	(0.00)	(0.03)	(0.01)
lnagfs	− 0.012	− 0.009**	− 0.040**	− 0.022*
	(0.01)	(0.00)	(0.02)	(0.01)
Cons	1.513***	2.494***	− 1.402***	3.028***
	(0.15)	(0.16)	(0.41)	(0.48)
N	1450	2030	1450	2030
R 2	0.064	0.349	0.030	0.411
City fixed effect	Yes	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes	Yes

Table 4.

Regression coefficients, statistical significance, sample size (N), and goodness of fit (R²) for two-way fixed-effect models (individual and time fixed effect) for ecological restoration and two kinds of urban-rural income gaps before and after 2012 in development zones to be optimized. The values in brackets under the coefficients are their cluster robust standard errors. See the Methods section for details. Cons means the regression y-intercept. Significance levels: ***p < 0.001, **p < 0.01, *p < 0.05

Variable	2007–2011 lngap	2012–2018 lngap	2007–2011 lntheil	2012–2018 lntheil
lnndvi	1.199**	− 0.253*	2.076	− 0.698**
	(0.44)	(0.11)	(1.07)	(0.24)
lnainv	0.223**	− 0.018	0.917***	− 0.041
	(0.09)	(0.01)	(0.30)	(0.02)
lnrden	− 0.018	0.003	− 0.015	0.009
	(0.01)	(0.00)	(0.05)	(0.01)
lnunemp	0.022	− 0.004	0.042	− 0.007
	(0.03)	(0.01)	(0.07)	(0.02)
lnurb	− 0.364**	− 0.113	− 2.067**	− 1.062***
	(0.15)	(0.08)	(0.48)	(0.18)
lnadoc	0.096	0.046	0.212	0.070
	(0.10)	(0.03)	(0.26)	(0.08)
lnaedus	0.010	− 0.103**	− 0.082	− 0.352***
	(0.08)	(0.04)	(0.23)	(0.08)
lnascis	− 0.114	− 0.018	− 0.234	− 0.038
	(0.07)	(0.01)	(0.24)	(0.03)
lnagfs	− 0.019	− 0.004	− 0.070	− 0.013
	(0.02)	(0.01)	(0.07)	(0.02)
Cons	− 0.067	2.131***	− 6.171*	4.302***
	(1.20)	(0.39)	(3.56)	(0.93)
N	215	301	215	301
R 2	0.129	0.264	0.160	0.537
City fixed effect	Yes	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes	Yes

Table 5.

Regression coefficients, statistical significance, sample size (N), and goodness of fit (R²) for two-way fixed-effect models (individual and time fixed effect) for ecological restoration and two kinds of urban-rural income gaps before and after 2012 in key development zones. The values in brackets under the coefficients are their cluster robust standard errors. See the Methods section for details. Cons means the regression y-intercept. Significance levels: ***p < 0.001, **p < 0.01, *p < 0.05

Variable	2007–2011 lngap	2012–2018 lngap	2007–2011 lntheil	2012–2018 lntheil
lnndvi	− 0.027	− 0.340***	0.025	− 0.593***
	(0.15)	(0.07)	(0.30)	(0.22)
lnainv	0.033	− 0.069***	0.028	− 0.095
	(0.02)	(0.02)	(0.04)	(0.06)
lnrden	0.001	0.013**	0.001	0.038**
	(0.01)	(0.01)	(0.01)	(0.01)
lnunemp	− 0.015*	− 0.002	− 0.026*	− 0.005
	(0.01)	(0.00)	(0.01)	(0.02)
lnurb	− 0.120**	− 0.114*	− 0.334**	− 0.859**
	(0.04)	(0.06)	(0.12)	(0.35)
lnadoc	− 0.021	− 0.017	0.004	− 0.067
	(0.02)	(0.01)	(0.04)	(0.04)
lnaedus	− 0.026	0.017	− 0.051	0.011
	(0.03)	(0.02)	(0.05)	(0.08)
lnascis	− 0.036**	− 0.005	− 0.060*	− 0.033
	(0.02)	(0.01)	(0.03)	(0.02)
lnagfs	− 0.015*	− 0.015*	− 0.034*	− 0.044**
	(0.01)	(0.01)	(0.02)	(0.01)
Cons	1.593***	2.273***	− 0.302	2.591***
	(0.22)	(0.24)	(0.52)	(0.80)
N	510	714	510	714
R 2	0.091	0.413	0.137	0.389
City fixed effect	Yes	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes	Yes

Model specification

We set the following two-way fixed-effect model to estimate the effect of ecological restoration on common prosperity:

$$\text{ln}{y}_{\mathit{it}}=a\text{ln}ndv{i}_{\mathit{it}}+{\mathbf{x}}_{\mathit{it}}{\mathbf{\beta }}_{}+{\gamma }_t+{\mu }_i+{\epsilon }_{\mathit{it}},$$ 1

where y_it represents common prosperity variables (i.e., municipal–provincial income gap and urban–rural income gap), a represents the regression coefficient of the parametric variable ndvi_it, x_it represents the control variables for city i in year t, γ_t represents year fixed effects, ${\mu }_{\text{i}}$ represents city fixed effects, and ε_it represents the error term. To produce normally distributed residuals in the model, we ln-transformed the variables.

It is necessary to test the robustness of the model. We tested it by adding control variables to the model and observed whether the sign of the coefficient for the core explanatory variable (lnndvi_it) and its statistical significance changed significantly; if not, this supports our belief that our results are robust, which can prove that the effect of ecological restoration on common prosperity will not be affected by other control variables. We also tested whether a nonlinear relationship exists by adding the quadratic term of ndvi_it in the model and checked its statistical significance (Tables 6 and 7).

Table 6.

Regression coefficients, statistical significance, sample size (N), and goodness of fit (R²) for two-way fixed-effect models (individual and time fixed effect) for ecological restoration and the municipal–provincial income gap in China. The values in brackets under the coefficients are their cluster robust standard errors. See the Methods section for details. Cons means the regression y-intercept. Significance levels: ***p < 0.001, **p < 0.01, *p < 0.05

Variable	(1)	(2)	(3)	(4)	(5)
lnndvi	− 0.107**	− 0.085**	− 0.081**	− 0.088**	− 0.120
	(0.04)	(0.04)	(0.04)	(0.04)	(0.08)
(lnndvi)2					− 0.015
					(0.03)
lnainv	0.007*	0.001	− 0.000	0.002	0.002
	(0.00)	(0.00)	(0.00)	(0.01)	(0.01)
lnrden	− 0.001	− 0.003	− 0.002	− 0.003	− 0.003
	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
lnunemp		− 0.000	− 0.000	0.000	0.000
		(0.00)	(0.00)	(0.00)	(0.00)
lnurb		0.384***	0.394***	0.393***	0.395***
		(0.02)	(0.02)	(0.02)	(0.02)
lnadoc			0.007	0.007	0.008
			(0.01)	(0.01)	(0.01)
lnaedus			0.012	0.020	0.020
			(0.01)	(0.01)	(0.01)
lnascis				− 0.010*	− 0.010*
				(0.01)	(0.01)
lnagfs				− 0.000	− 0.000
				(0.01)	(0.01)
Cons	− 0.157***	− 1.483***	− 1.609***	− 1.646***	− 1.669***
	(0.05)	(0.08)	(0.15)	(0.15)	(0.16)
N	3480	3480	3480	3480	3480
R2	0.102	0.387	0.388	0.391	0.391
City fixed effect	Yes	Yes	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes	Yes	Yes

Table 7.

Regression coefficients, statistical significance, sample size (N), and goodness of fit (R²) for two-way fixed-effect models (individual and time fixed effect) for ecological restoration and the urban–rural income gap in China. The values in brackets under the coefficients are their cluster robust standard errors. See the Methods section for details. Cons means the regression y-intercept. Significance levels: ***p < 0.001, **p < 0.01, *p < 0.05

Variable	(1)	(2)	(3)	(4)	(5)
lnndvi	− 0.369***	− 0.338***	− 0.283***	− 0.288***	− 0.173**
	(0.04)	(0.04)	(0.04)	(0.04)	(0.06)
(lnndvi)2					0.017
					(0.01)
lnainv	− 0.098*	− 0.079*	− 0.050*	− 0.041*	− 0.023
	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)
lnrden	0.000	0.001	0.001	0.003	0.002
	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
lnunemp		− 0.000	− 0.000	− 0.004	− 0.004
		(0.00)	(0.00)	(0.00)	(0.00)
lnurb		− 0.118***	− 0.123***	− 0.121***	− 0.135***
		(0.03)	(0.03)	(0.03)	(0.04)
lnadoc			− 0.045***	− 0.042***	− 0.028**
			(0.01)	(0.01)	(0.01)
lnaedus			− 0.040***	− 0.022**	− 0.033*
			(0.01)	(0.01)	(0.02)
lnascis				− 0.019***	− 0.023***
				(0.01)	(0.01)
lnagfs				− 0.007	0.015
				(0.00)	(0.01)
Cons	2.137***	2.327***	2.368***	2.261***	1.898***
	(0.11)	(0.11)	(0.11)	(0.12)	(0.23)
N	3480	3480	3480	3480	3480
R2	0.370	0.383	0.398	0.402	0.419
City fixed effect	Yes	Yes	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes	Yes	Yes

Results

Our results showed that ecological restoration had a statistically significant negative effect on the municipal–provincial income gap and urban–rural income gap in China, suggesting that restoration could help achieve “prosperity” and “common prosperity.” Our robustness test showed that the sign of the coefficient for the core explanatory variable (lnndvi_it) and its statistical significance did not change significantly. Therefore, it was proven that our results are convincing (Tables 6 and 7). We also found that the quadratic term of lnndvi_it is not statistically significant in Tables 6 and 7, suggesting that the effect of ecological restoration on the income gap (both kinds) is linear.

For control variables, we found that investment, urbanization, and infrastructure construction (except transportation factor) showed a statistically significant positive effect on narrowing the urban–rural income gap in China (Table 7). However, urbanization could widen the municipal–provincial income gap in China (Table 6). In addition, it was also suggested that the transportation factor, unemployment, and government fiscal expenditure did not show a statistically significant effect on either kind of income gap for all of China during the study period (Tables 6 and 7).

Our heterogeneity analysis results showed that the effect of ecological restoration on the urban–rural income gap differed before and after 2012. For China as a whole, the effect was not statistically significant before 2012 but was statistically significant and negative after 2012 (Table 3). The results of key development zones are similar to those of all of China (Table 4), but the results of development zones to be optimized showed a difference: the effect was statistically significant and positive before 2012 and statistically significant and negative after 2012, suggesting that ecological restoration could even widen the urban–rural income gap in development zones to be optimized before 2012. The results of replacing the urban–rural income gap with the Theil index are similar to the results above, suggesting that our results are robust and convincing (Table 5).

Discussion

Ecosystem degradation and a serious wealth gap have become problems that cannot be neglected during the progress of pursuing modernization and sustainable development in China. Our results revealed that the relationship between ecological restoration and common prosperity could vary over time and it is possible to promote common prosperity through ecological restoration. This is meaningful because the conflict between natural protection and rural livelihood development in forest areas has long persisted, especially in nature reserves and the surrounding communities (Ma et al. 2018; Wang 2017). Low-income rural residents in remote rocky mountainous, border, and minority areas are poor and rely on products of natural forests such as timber, herbs, and fruits for their livelihood (Liu and Li 2017; Sietz et al. 2011). These natural resources have been proven to account for nearly 20%-30% of total household income (Angelsen et al. 2014; Langat et al. 2016; Suleiman et al. 2017; Tugume et al. 2015; Vedeld et al. 2007). The implementation of ecological conservation projects and policies limits the use of natural resources, which forces rural livelihoods to change their traditional way of life because part of their income has been cut off (Wang et al. 2013), which may explain why ecological restoration could widen the urban–rural income gap in development zones to be optimized before 2012. However, previous research has suggested that low-income families experience faster income growth from local tourism development, which could contribute to alleviating income inequality (e.g., Das and Rainey 2010; Wang and Liu 2018; Zhang et al. 2021). This may provide a logical explanation for why ecological restoration and the narrowing of the urban–rural income gap can coexist harmoniously in China over time.

Unfortunately, the process of pursuing the common prosperity of urban and rural residents through ecological restoration may be challenging because of some practical obstacles (e.g., employment opportunities, educational attainment, social discrimination). For example, the government provides subsidies to rural residents affected by ecological protection to compensate for their losses, and these funds even improve their economic level for a short time (Uchida et al. 2005), but there are still people who cannot find a new job or work in the low-end private sector due to the lack of training for the skills required by new green industries (Kuhn and Shen 2015). Although our results in Tables 6 and 7 show that the unemployment rate showed no statistically significant effect on “prosperity” (municipal–provincial income gap) and “common prosperity” (urban–rural income gap), it has been revealed that the Chinese government has never collected statistics on the unemployment of migrant workers, and such neglect of the problems they face reinforces the fragility of their livelihoods (Zhu 2002). Therefore, the real unemployment situation may be more serious than the statistical number, which may affect the accuracy of our empirical results. Fortunately, rapid urbanization in China, which requires a large labor supply, can increase employment opportunities for unemployed rural residents and narrow the urban–rural income gap (Table 7). Therefore, it is necessary for the government to fund green industries, provide more targeted technical training and guidance for the poor and help them improve the quality of their jobs, narrowing the income gap with the rich via more efficient approaches (Li et al. 2017). Furthermore, given the huge amount of rural migrant workers, which is nearly 0.3 billion (Zhang et al. 2022b), it is necessary for the government to let them who have a stable job register as urban residents and gain access to a broad range of social services such as free medical care and education for their children.

In addition, we found that government fiscal expenditure did not show a statistically significant effect on either income gap. This may be because the relationship between low-income rural livelihoods and ecosystems is complex and geographically dependent (Wu et al. 2020), which requires the government and managers to consider alternatives based on the constraints created by local conditions (e.g., economic development level, environmental carrying capacity, access to productive farmland, and labor availability), thereby maximizing the economic benefits local residents obtain from ecological restoration rather than simply using laws and administrative measures to formulate protection resolutions or policies (Hosseininia et al. 2013; Shukla and Sinclair 2010). For instance, in successful case studies, Cao et al. (2020) described that rural resident obtained long-term income increases by developing profitable industries such as fish farming, establishing plantations of valuable herbs, cultivating fruit trees, and raising animals such as pigs in barns with the help of the local government.

In summary, reducing income inequality through ecological restoration will be a time-consuming process and requires the constant effort of the Chinese government and local managers. A well-functioning ecological environment may enhance the value of ecological products and increase the income of rural residents, which is a possible internal mechanism of such an effect. However, it is challenging to convert the added value of NDVI to biomass values. Therefore, the test of such a mechanism cannot be realized for now. In our future research, we will focus on how to address that problem. Furthermore, we will also test the change in the effect of ecological restoration on common prosperity in limited development zones and prohibited development zones over time when the relevant data are available to complete our research. At last, more interesting and meaningful results may be found if we use biodiversity as another ecological factor instead of NDVI. Unfortunately, we cannot find available data at the level of prefecture-level cities, but it may be feasible to conduct such research at a level of natural reserves (e.g., Giant panda reserves) in China.

Biographies

Zihao Ma

is a student at Beijing Normal University. His interests include ecological economics, institutional economics, and politics.

Xin Tian

is an Associate Professor at Beijing Normal University. Her interests include environmental economics, ecological system, and management.

Pingdan Zhang

is the corresponding author. He is a Professor at Beijing Normal University. His interests include ecological economics and politics.

Author contributions

PZ designed the research; ZM, XT, and PZ analyzed the data and wrote the paper. All authors have approved the paper.

Declarations

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

The opinions expressed here are those of the authors and do not necessarily reflect the position of the Government of China or any other organization.