Can the Low-Carbon Transition Impact the Urban–Rural Income Gap? Empirical Evidence from the Low-Carbon City Pilot Policy
Abstract
:1. Introduction
2. Literature Review
2.1. Research Progress of Low-Carbon Cities and LCCP Policies
2.2. Progress of Research on Factors Influencing Urban–Rural Income Disparity
2.3. Advances in Research on the Social Impact of Environmental Regulation
3. Theoretical Model and Research Hypotheses
3.1. A Theoretical Model of Low-Carbon City Pilot Affecting the Urban–Rural Income Gap
3.2. Mediating Mechanisms of LCCP Policies Affecting Urban–Rural Income Disparity
4. Model Setting and Variable Selection
4.1. Selection of Variables and Data Source
- (1)
- Urban–rural income gap: The Teil index (Teil) is used in this article to measure the urban-rural income disparity. Ji Xuanming et al. (2021) [31] argue that Teil index has more excellent characteristics compared with Gini coefficient. There are three reasons. First, the Teil index can fully take into account the effect of the population base. Second, the Gini coefficient is only sensitive to changes in the middle of the data and does not respond to changes at either end of the data. The Teil index is sensitive to “long tail” data and does not respond to changes in the middle of the data. The gap between rich and poor is reflected at both the high and low income ends of the spectrum. Third, the Teil index can be decomposed into between- and within-groups depending on who is measured. Thus, the Teil index can not only reflect the gap between the high- and low-income ends of the spectrum, but also measure intergroup data well. Therefore, this paper refers to the study of Chen Xu (2019) [32] and uses the Teil index to represent the urban–rural income gap. In the robustness test, this paper uses the urban–rural income ratio (Gapre).
- (2)
- Employment structure effect (LABOR): Referring to the research by Xuan Ye et al. (2019) [33], it is expressed by the ratio of employees in information transmission computer services and software, employees in finance, employees in leasing and business services, and personnel in scientific research, technical services, and geological exploration as a percentage of total employees.
- (3)
- The overall regional innovation level (INNR): Referring to the research by Tang Song et al. (2020) [34], to assess regional innovation capability, the number of regional patent applications was employed. It is expressed by the logarithm of the total number of regional patent applications.
- (4)
- Control variables: Given the complexities of the factors influencing the urban-rural income disparity, this article divides the control variables into macroeconomic growth factors and social development elements. (1) Per capita GDP (PGDP), given as the logarithm of per capita GDP, is a macroeconomic growth factor. (2) Science and technology expenditures (FINT), as measured by the logarithm of science and technology fiscal expenditures. (3) Education expenditure (FI-NE), calculated as the logarithm of education spending in fiscal expenditure. (4) External openness (OPEN), as assessed by the ratio of total actual foreign capital employed to GDP. (5) The degree of financial development (BANK) as assessed by the loan-to-deposit ratio. Population size (POP), calculated as the logarithm of the total population at the end of the year, is one of the social development elements. (2) The urban registered jobless rate (EMP), calculated as the ratio of urban registered unemployed to total population at the end of the year.
4.2. Model Setting Subsection
4.3. Data Introduction
5. Methodology and Materials
5.1. Baseline Model Regression Result
5.2. Parallel Trend Test
5.3. Robustness Check
- (1)
- Replacing the explanatory variable metrics.
- (2)
- Excluding other policy effects.
- (3)
- Propensity Score Matching Differences-in-Differences model (PSM-DID).
- (4)
- Instrumental Variable Analysis
5.4. Heterogeneity Analysis
- (1)
- Locational heterogeneity. There are large regional differences in China, with the eastern region developing rapidly, having natural ports, better levels of technological innovation, foreign direct investment, and infrastructure than the central and western regions, which also causes a large number of laborers from the central and western regions to move to the eastern region. As a result, following on the work of Li Yanling et al. (2022) [38], this study divides the sample into eastern, central, and western sectors in order to evaluate the various effects of building low-carbon cities in different locations on the urban-rural income gap. As a consequence, based on the regression findings provided in Table 5, the LCCP policies have a considerable expanding influence on the urban-rural income difference in the eastern area. The urban-rural income disparity in eastern low-carbon cities is 0.0077 units more than in non-pilot cities, and the coefficient is significant at the 1% level. However, the coefficients of LCCP policies are not significant in either the central or western regions. It is strongly suggested that the phenomenon of widening urban–rural income gaps due to LCCP policies mainly arises in the eastern region, which also proves the existence of locational heterogeneity. In addition, the coefficient of the LCCP variable is 0.005 for the full sample (column 4 of Table 2) and 0.0077 for the eastern region. The comparison results indicate that the LCCP policies have a greater impact on widening the urban–rural income gap in the eastern region.
- (2)
- City-scale heterogeneity. LCCP policies’ impact on the urban-rural income gap probably varies by city size. As shown in Table 5, the effect of LCCP policies implementation on urban–rural income gap is manifested in mega-cities, but not yet significant in other cities. It is possible that the development of mega-cities basically relies on the service industry, and the LCCP policies enable the industrial structure upgrading effect to be more apparent, which further widens the urban–rural income gap.
- (3)
- Resource endowment heterogeneity. Wen Shiyan et al. (2022) [39] point out that the resource endowments on which urban development depend are the basis for influencing urban innovation. In this regard, this paper examines the heterogeneous impact of LCCP policies on urban–rural income disparity from the perspective of urban resource endowment differences. Based on the Notice of the State Council on the Issuance of the National Sustainable Development Plan for Resource-based Cities (2013–2020) (Guo Fa (2013) No. 45), this paper divides the 282 sample cities into 113 resource cities and 169 non-resource cities. Observing columns (6) and (7) of Table 5, it can be observed that the LCCP policies widen the urban–rural income gap in non-resource cities and have a negative effect on resource cities, i.e., they can reduce the urban–rural income gap, but this effect does not pass the 10% significance level test, a finding similar to the scholar Gao Da et al. (2022) [40]. Compared with resource-endowed cities, the development of non-resource-based cities relies more on the optimization of industrial structure, and under the influence of LCCP policies, more attention is paid to the scale expansion of tertiary industries as well as the innovative development of secondary industries, thus significantly widening the urban–rural income gap.
5.5. Mediating Effect Analysis
- (1)
- Structural effect
- (2)
- Innovation effect
6. Conclusions and Policy Recommendations
6.1. Conclusions
6.2. Policy Recommendations
6.3. Deficiencies and Prospects
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
References
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Variables | N | Mean | SD | Min | Max |
---|---|---|---|---|---|
Policy variables | |||||
Time | 4230 | 0.281 | 0.450 | 0 | 1 |
Treat | 4230 | 0.425 | 0.494 | 0 | 1 |
Core independent variables | |||||
Theil | 4230 | 0.0860 | 0.0516 | 0.00220 | 0.397 |
Gapre | 4230 | 2.459 | 0.571 | 1.196 | 6.201 |
Control variable | |||||
PGDP | 4230 | 10.52 | 0.653 | 8.113 | 12.29 |
POP | 4230 | 15.10 | 0.683 | 12.11 | 17.36 |
OPEN | 4230 | 20.82 | 2.101 | 11.00 | 26.05 |
FINT | 4230 | 19.34 | 1.472 | 15.36 | 24.57 |
FINE | 4230 | 22.10 | 0.887 | 18.49 | 25.46 |
BANK | 4230 | 0.671 | 0.218 | 0.207 | 8.325 |
EMP | 4230 | 0.00600 | 0.00410 | 0.000400 | 0.0447 |
Variables for mechanism analysis | |||||
LABOR | 4230 | 0.0814 | 0.0340 | 0.00940 | 0.370 |
INNA | 4230 | 0.0433 | 0.106 | 0.000100 | 1.317 |
Variables | ||||
---|---|---|---|---|
LCCP | −0.0265 *** | −0.0080 *** | −0.0060 | 0.0050 * |
(−16.7652) | (−6.2414) | (−1.6429) | (1.9658) | |
PGDP | −0.0500 *** | −0.0421 *** | −0.0250 *** | |
(−21.1769) | (−6.9071) | (−5.1788) | ||
POP | −0.0046 ** | −0.0182 *** | 0.0258 ** | |
(−2.2123) | (−2.6113) | (2.3388) | ||
OPEN | −0.0029 *** | −0.0049 *** | −0.0010 ** | |
(−6.4186) | (−4.4434) | (−2.1323) | ||
FINT | −0.0034 *** | −0.0069 *** | 0.0011 | |
(−3.5817) | (−3.1763) | (0.7823) | ||
FINE | 0.0130 *** | 0.0351 *** | −0.0145 *** | |
(7.6153) | (5.4544) | (−2.7670) | ||
BANK | 0.0057 * | 0.0075 | 0.0042 | |
(1.9465) | (1.0062) | (1.5073) | ||
EMP | −1.2150 *** | −1.7619 *** | 0.3402 | |
(−8.2607) | (−3.5877) | (1.0688) | ||
Constant | 0.0934 *** | 0.5249 *** | 0.2704 *** | 0.2739 |
(97.9055) | (21.6072) | (3.6068) | (1.5263) | |
0.0533 | 0.4990 | 0.5435 | 0.9077 | |
City fixed effect | No | No | No | Yes |
Year fixed effect | No | No | Yes | Yes |
N | 4230 | 4230 | 4230 | 4230 |
Variables | (1) Gapre | |
---|---|---|
LCCP | 0.0531 * | 0.0044 * |
(1.6661) | (1.7726) | |
Innov * post | 0.0108 *** | |
(3.8233) | ||
PGDP | −0.2608 *** | −0.0242 *** |
(−4.6825) | (−5.0893) | |
POP | 0.1484 | 0.0260 ** |
(1.1947) | (2.3491) | |
OPEN | −0.0058 | −0.0009 ** |
(−1.0755) | (−2.0203) | |
FINT | 0.0087 | 0.0010 |
(0.4969) | (0.6952) | |
FINE | −0.1820 *** | −0.0158 *** |
(−2.6528) | (−3.0204) | |
BANK | 0.0613 * | 0.0035 |
(1.6736) | (1.3447) | |
EMP | 5.0941 | 0.2639 |
(1.2410) | (0.8375) | |
Constant | 6.8490 *** | 0.2914 |
(3.2201) | (1.6145) | |
0.8836 | 0.9089 | |
City effect | Yes | Yes |
Year effect | Yes | Yes |
N | 4230 | 4230 |
Variables | PSM-DID | 2SLS-IV | |
---|---|---|---|
(2) LCCP | |||
LCCP | 0.0046 * | 0.0070 *** | |
(1.8306) | (3.0628) | ||
lnVC * post | 0.5494 *** | ||
(31.9308) | |||
PGDP | −0.0251 *** | 0.0947 *** | −0.0403 *** |
(−5.1671) | (2.9838) | (−6.3964) | |
POP | 0.0262 ** | −0.0288 | 0.0143 |
(2.3190) | (−0.5607) | (1.2237) | |
OPEN | −0.0010 ** | −0.0026 | −0.0004 |
(−2.1596) | (−1.1032) | (−0.6951) | |
FINT | 0.0010 | 0.0205 ** | 0.0007 |
(0.7445) | (2.5580) | (0.4754) | |
FINE | −0.0148 *** | 0.0596 *** | −0.0117 ** |
(−2.7962) | (2.7229) | (−2.2464) | |
BANK | 0.0115 | −0.0026 | 0.0038 |
(1.5228) | (−0.0498) | (0.4086) | |
EMP | 0.3403 | −0.8663 | 0.3171 |
(1.0792) | (−0.5518) | (1.0217) | |
Constant | 0.2706 | ||
(1.4929) | |||
0.9077 | 0.6351 | ||
City effect | Yes | Yes | Yes |
Year effect | Yes | Yes | Yes |
N | 4216 | 3384 | 3384 |
Kleibergen–Paap rk LM | 154.16 [0.0000] | ||
Cragg-Donald Wald F | 2.1e + 04 {16.38} |
Variables | (1) Based on Three Regions | (2) Based on City Size | (3) Based on City Resource Endowment | ||||
---|---|---|---|---|---|---|---|
East | Central | West | Large Cities | Other Cities | Resource Cities | Non-resource Cities | |
LCCP | 0.0077 *** | 0.0011 | −0.0079 | 0.0078 ** | 0.0037 | −0.0049 | 0.0083 *** |
(3.0760) | (0.2845) | (−1.2139) | (2.0996) | (1.0925) | (−0.9229) | (3.1117) | |
PGDP | −0.0054 | −0.0026 | −0.0565 *** | −0.0220 ** | −0.0259 *** | −0.0236 *** | −0.0252 *** |
(−1.0330) | (−0.7174) | (−7.0719) | (−2.2277) | (−4.6707) | (−3.6759) | (−3.7953) | |
POP | 0.0493 ** | 0.0350 ** | −0.0526 * | 0.0118 | 0.0211 | 0.0159 | 0.0312 *** |
(2.2658) | (2.0241) | (−1.8877) | (0.9133) | (1.3143) | (0.5734) | (2.7358) | |
OPEN | −0.0016 * | −0.0017 ** | 0.0005 | −0.0020 | −0.0009 * | −0.0008 | −0.0010 |
(−1.6725) | (−2.3143) | (0.8134) | (−1.2358) | (−1.9069) | (−1.3924) | (−1.5321) | |
FINT | −0.0029 * | −0.0030 | 0.0078 *** | −0.0013 | 0.0016 | 0.0027 | −0.0014 |
(−1.7817) | (−1.5760) | (3.1600) | (−0.3555) | (1.0724) | (1.1645) | (−0.8980) | |
FINE | −0.0237 *** | −0.0044 | −0.0117 | −0.0175 * | −0.0151 ** | −0.0254 *** | −0.0099 |
(−3.7802) | (−0.4986) | (−1.2678) | (−1.8065) | (−2.3443) | (−2.8165) | (−1.6153) | |
BANK | 0.0192 ** | 0.0016 | 0.0217 | 0.0032 | 0.0040 | 0.0043* | 0.0046 |
(2.1712) | (1.2736) | (1.4690) | (0.2482) | (1.5462) | (1.8098) | (0.6309) | |
EMP | 0.7302 | 0.2029 | 0.7863 | 0.4524 | 0.3476 | 0.2832 | 0.3504 |
(1.1689) | (0.3294) | (1.5127) | (0.6165) | (0.9696) | (0.7144) | (0.7128) | |
Constant | −0.0297 | −0.2299 | 1.5681 *** | 0.5889 * | 0.3612 | 0.6235 | 0.1309 |
(−0.0933) | (−1.0291) | (3.6239) | (1.8287) | (1.5202) | (1.5763) | (0.6525) | |
0.8746 | 0.8949 | 0.9212 | 0.9098 | 0.9082 | 0.9056 | 0.9098 | |
City effect | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Year effect | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
N | 1470 | 1500 | 1245 | 1310 | 2917 | 1695 | 2535 |
Variables | (1) LABOR | |
---|---|---|
LCCP | 0.0038 * | 0.0043 * |
(1.9311) | (1.7339) | |
The square of the LABOR | 0.6829 *** | |
(2.8126) | ||
LABOR | −0.0874 | |
(−1.3143) | ||
PGDP | −0.0077 ** | −0.0243 *** |
(−2.3229) | (−5.0830) | |
POP | −0.0129 | 0.0261 ** |
(−1.2849) | (2.3434) | |
OPEN | −0.0003 | −0.0010 ** |
(−0.7258) | (−2.1930) | |
FINT | 0.0003 | 0.0011 |
(0.2472) | (0.7986) | |
FINE | −0.0097 ** | −0.0141 *** |
(−2.2836) | (−2.6901) | |
BANK | −0.0003 | 0.0042 |
(−0.1427) | (1.5161) | |
EMP | −0.0682 | 0.3564 |
(−0.2563) | (1.1397) | |
Constant | 0.5723 *** | 0.2524 |
(3.6493) | (1.3933) | |
0.7860 | 0.9085 | |
City effect | Yes | Yes |
Year effect | Yes | Yes |
N | 4230 | 4230 |
Variables | (1) INNR | |
---|---|---|
LCCP | 0.0351 *** | 0.0025 |
(3.4630) | (1.0080) | |
The square of the INNR | −0.0478 ** | |
(−2.4384) | ||
INNR | 0.1082 *** | |
(4.6214) | ||
PGDP | −0.0305 ** | −0.0226 *** |
(−2.4616) | (−4.9687) | |
POP | 0.1701 ** | 0.0126 |
(2.5256) | (0.8818) | |
OPEN | 0.0001 | −0.0009 ** |
(0.1248) | (−2.1029) | |
FINT | 0.0090 *** | 0.0003 |
(2.9502) | (0.1972) | |
FINE | 0.0153 | −0.0162 *** |
(0.9703) | (−3.1398) | |
BANK | −0.0045 | 0.0041 |
(−0.5157) | (1.6338) | |
EMP | 0.9487 | 0.2523 |
(0.5493) | (0.8346) | |
Constant | −2.7326 *** | 0.4957 ** |
(−3.0289) | (2.2625) | |
R2 | 0.8057 | 0.9116 |
City effect | Yes | Yes |
Year effect | Yes | Yes |
N | 4230 | 4230 |
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Chen, T.; Zhang, Z. Can the Low-Carbon Transition Impact the Urban–Rural Income Gap? Empirical Evidence from the Low-Carbon City Pilot Policy. Sustainability 2023, 15, 5726. https://doi.org/10.3390/su15075726
Chen T, Zhang Z. Can the Low-Carbon Transition Impact the Urban–Rural Income Gap? Empirical Evidence from the Low-Carbon City Pilot Policy. Sustainability. 2023; 15(7):5726. https://doi.org/10.3390/su15075726
Chicago/Turabian StyleChen, Tingwei, and Zongbin Zhang. 2023. "Can the Low-Carbon Transition Impact the Urban–Rural Income Gap? Empirical Evidence from the Low-Carbon City Pilot Policy" Sustainability 15, no. 7: 5726. https://doi.org/10.3390/su15075726