Spatial Effects of Economic Modernization on Carbon Balance in China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Framework for Analysis
2.2. Data Sources
2.3. Construction of the Indicator System for the Three Modernizations
2.4. Carbon Balance Ratio (CBR)
2.5. Control Variables
- (1)
- Level of economic development (GDPPC). This is gauged by the per capita GDP of each province and city.
- (2)
- Level of innovation. This is assessed by the count of effective invention patents in each province and city.
- (3)
- Openness. This is evaluated by the total foreign investment in each province and city.
- (4)
- Infrastructure. This is measured by the density of highways in each province and city.
2.6. Spatial Autocorrelation Test
2.7. Spatial Durbin Model (SDM)
3. Results
3.1. Spatial and Temporal Variations in CBR
3.2. Spatial Distribution of Agricultural Modernization, Industrialization, and Urbanization
3.3. Spatial Autocorrelation Test
3.4. Spatial Spillover Effects of the CBR
3.5. SDM Model Regression Results
3.6. Robustness Testing
3.6.1. One-Period Lagged Explanatory Variables
3.6.2. Exclusion of Some Samples
3.7. Heterogeneity Analysis
4. Discussion
4.1. Spatial Spillover Effects of Three Modernizations on the Carbon Balance in China
4.2. Divergence of Spatial Spillover Effects of Infrastructure Development and Innovation Levels on the Carbon Balance
4.3. Regional Heterogeneity in the Spatial Effects of Agricultural Modernization and Industrialization on the Carbon Balance
4.4. Policy Recommendations
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Types | Indicators | Description | Attributes |
---|---|---|---|
Agricultural Modernization Index (AMI) | Per capita disposable income of rural residents (CNY/person) [44] | + | |
Engel’s coefficient of rural residents (%) [49] | Rural residents’ food expenditures as a percentage of consumption expenditures | − | |
Degree of agricultural mechanization (kW/ha) [49] | Total power of agricultural machinery divided by area of cultivated land | + | |
Grain yield (t/ha) [44] | Grain production divided by area sown to grain | + | |
Industrialization Index (II) | Number of industrial enterprises above scale (number) [10] | + | |
Industrial profit (million CNY) [50] | + | ||
R&D Expenditure (%) [10] | R&D expenditure as a percentage of regional GDP | + | |
Industrialization rate (%) [51] | Value added of industry as a percentage of GDP | + | |
Urbanization Index (UI) | Share of urban population (%) [10] | Urban population as a percentage of total regional population | |
Per capita disposable income of urban residents (CNY/person) [52] | + | ||
Engel’s coefficient of urban residents (%) [52] | Urban residents’ food expenditure as a percentage of consumption expenditure | - | |
Employment urbanization rate (%) [53] | Urban employment as a percentage of total employment | + |
Land Types | Forestland | Grassland | Water | Unused Land | Wetland |
---|---|---|---|---|---|
Carbon absorption coefficient | 0.0578 | 0.0021 | 0.0252 | 0.0005 | 0.00006132 |
Unit | kg/(m2·a) | kg/(m2·a) | kg/(m2·a) | kg/(m2·a) | kg/(m2·a) |
Year | Carbon Sink (108 t) | Carbon Emission (108 t) | CBR |
---|---|---|---|
2010 | 1.4952 | 1.0971 | 1.3628 |
2011 | 1.4974 | 1.0775 | 1.3898 |
2012 | 1.4971 | 1.1378 | 1.3158 |
2013 | 1.4935 | 1.1379 | 1.3125 |
2014 | 1.4913 | 1.1436 | 1.3040 |
2015 | 1.4918 | 1.1431 | 1.3050 |
2016 | 1.4937 | 1.1441 | 1.3055 |
2017 | 1.4967 | 1.1498 | 1.3017 |
2018 | 1.4978 | 1.1599 | 1.2914 |
2019 | 1.5009 | 1.1716 | 1.2811 |
2020 | 1.5002 | 1.1739 | 1.2780 |
2021 | 1.5015 | 1.1884 | 1.2635 |
Year | I | z | p-Value | Year | I | z | p-Value |
---|---|---|---|---|---|---|---|
2010 | 0.084 | 3.184 | 0.001 | 2016 | 0.080 | 3.096 | 0.001 |
2011 | 0.085 | 3.221 | 0.001 | 2017 | 0.076 | 2.996 | 0.001 |
2012 | 0.087 | 3.264 | 0.001 | 2018 | 0.073 | 2.929 | 0.002 |
2013 | 0.085 | 3.216 | 0.001 | 2019 | 0.075 | 2.980 | 0.001 |
2014 | 0.084 | 3.186 | 0.001 | 2020 | 0.068 | 2.818 | 0.002 |
2015 | 0.080 | 3.086 | 0.001 | 2021 | 0.076 | 2.991 | 0.001 |
Test | Statistic | |
---|---|---|
Spatial error: | ||
LM | Lagrange multiplier | 693.614 *** |
Robust Lagrange multiplier | 221.39 *** | |
Spatial lag: | ||
Lagrange multiplier | 489.310 *** | |
Robust Lagrange multiplier | 17.090 *** | |
Hausman | 17.64 ** | |
LR | Lagrange multiplier | 101.48 *** |
Robust Lagrange multiplier | 93.88 *** | |
Wald | Lagrange multiplier | 19.01 *** |
Robust Lagrange multiplier | 19.17 *** |
CBR | Direct | Indirect | Total |
---|---|---|---|
AMI | −1.284 *** (−3.30) | −3.401 *** (−2.680) | −4.686 *** (−3.990) |
II | −2.529 *** (−4.480) | 6.217 ** (2.190) | 3.688 (1.250) |
UI | −0.576 *** (−4.190) | −9.304 *** (−4.860) | −9.880 *** (−5.170) |
GDPPC | 0.148 (0.460) | 5.756 *** (3.580) | 5.903 *** (3.680) |
Openness | −0.148 *** (−3.550) | 0.891 *** (3.510) | 0.742 *** (3.510) |
Infrastructure | −0.470 *** (−2.930) | 2.807 *** (4.130) | 2.337 *** (3.470) |
Innovation | 2.520 *** (4.770) | −9.943 *** (−3.530) | −7.422 ** (−2.530) |
Spatial rho | −0.833 *** (−3.300) | ||
sigma2_e | 0.144 *** (13.170) |
CBR | Direct | Indirect | Total |
---|---|---|---|
L1_AMI | −0.601 * (−1.800) | −3.534 *** (−3.950) | −4.135 *** (−5.340) |
L1_II | −1.583 *** (−3.440) | 3.872 ** (2.200) | 2.289 (1.260) |
L1_UI | −0.877 * (−1.760) | −9.523 *** (−5.550) | −10.401 *** (−6.140) |
GDPPC | 0.370 (1.090) | 5.979 *** (4.380) | 6.349 *** (4.670) |
Openness | −0.153 *** (−3.610) | 0.839 *** (4.400) | 0.686 *** (3.870) |
Infrastructure | −0.389 ** (−2.440) | 2.158 *** (3.970) | 1.769 *** (3.500) |
Innovation | 1.607 *** (3.720) | −7.410 *** (−4.340) | −5.803 *** (−3.310) |
Spatial rho | −1.098 *** (−4.390) | ||
sigma2_e | 0.139 *** (13.130) |
CBR | Direct | Indirect | Total |
---|---|---|---|
AMI | −1.766 *** (−5.240) | −5.274 *** (−3.320) | −7.040 *** (−4.570) |
II | −1.720 *** (−2.780) | 13.134 *** (2.820) | 11.414 ** (2.390) |
UI | −0.545 *** (−3.890) | −14.966 *** (−3.760) | −15.511 *** (−3.840) |
GDPPC | 1.144 *** (2.920) | 15.732 *** (4.840) | 16.876 *** (4.960) |
Openness | −0.015 (−0.370) | 1.283 *** (3.870) | 1.268 *** (3.760) |
Infrastructure | −1.004 *** (−5.500) | 2.053 * (1.850) | 1.049 (0.900) |
Innovation | 1.175 *** (3.070) | −18.149 *** (−3.860) | −16.398 *** (−3.370) |
Spatial rho | −0.274 *** (−3.230) | ||
sigma2_e | 0.121 *** (12.500) |
Eastern Region | Central and Western Regions | |||||
---|---|---|---|---|---|---|
CBR | Direct | Indirect | Total | Direct | Indirect | Total |
AMI | −2.119 ** (−2.490) | −13.854 *** (−4.250) | −15.973 *** (−4.700) | −1.888 *** (−5.510) | 3.369 *** (3.260) | 1.481 (1.600) |
II | 5.705 *** (4.300) | 19.458 *** (3.600) | 25.164 *** (3.920) | −5.227 *** (−9.010) | 2.950 (1.160) | −2.277 (−0.860) |
UI | −1.900 *** (−2.680) | −11.281 *** (−4.510) | −13.181 *** (−4.840) | −1.835 *** (−2.580) | −5.890 *** (−2.600) | −7.725 *** (−3.400) |
GDPPC | 1.800 *** (2.650) | 9.528 *** (3.650) | 11.329 *** (3.820) | 0.922 ** (2.470) | −1.130 (−0.750) | −0.208 (−0.130) |
Openness | −0.487 * (−1.780) | 2.906 *** (4.020) | 2.419 *** (2.840) | 0.000 (0.010) | −0.102 (−0.590) | −0.102 (−0.580) |
Infrastructure | −1.238 *** (−2.890) | −0.508 (−0.450) | −1.746 (−1.390) | −0.312 * (−1.910) | 2.083 *** (3.870) | 1.771 *** (3.050) |
Innovation | −3.569 *** (−2.890) | −19.668 *** (−3.990) | −23.238 *** (−3.990) | 4.274 *** (8.340) | −3.087 (−1.320) | 1.187 (0.480) |
Spatial rho | −0.439 ** (−2.060) | −0.683 *** (−2.920) | ||||
sigma2_e | 0.046 *** (8.120) | 0.044 *** (9.480) |
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Huang, N.; Liu, C.; Liu, Y.; Giannetti, B.F.; Bai, L. Spatial Effects of Economic Modernization on Carbon Balance in China. Land 2024, 13, 595. https://doi.org/10.3390/land13050595
Huang N, Liu C, Liu Y, Giannetti BF, Bai L. Spatial Effects of Economic Modernization on Carbon Balance in China. Land. 2024; 13(5):595. https://doi.org/10.3390/land13050595
Chicago/Turabian StyleHuang, Nan, Chenghao Liu, Yaobin Liu, Biagio Fernando Giannetti, and Ling Bai. 2024. "Spatial Effects of Economic Modernization on Carbon Balance in China" Land 13, no. 5: 595. https://doi.org/10.3390/land13050595