Decoupling Effect, Driving Factors and Prediction Analysis of Agricultural Carbon Emission Reduction and Product Supply Guarantee in China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Measurement Method of ACE
2.2. “Two-Stage Rolling” Tapio Decoupling Model
2.3. Measuring the Development Level of Digital Economy
2.4. Spatial Econometric Model
2.4.1. Spatial Auto-Correlation Test
2.4.2. Spatial Econometric Model
2.5. Grey Prediction Model
2.6. Variable Selection and Data Sources
3. Results
3.1. Temporal and Spatial Distribution Characteristics of Decoupling Effect between Agricultural Carbon Emission Reduction and Product Supply Guarantee
3.2. Spatial Connection Intensity of Green Technology Innovation, ACE and APS in 2020
3.3. Confirmation of Spatial Econometric Model
3.3.1. Spatial Auto-Correlation Analysis
3.3.2. Inspection and Selection of Spatial Panel Econometric Model
3.4. Regression Analysis of Dynamic Spatial Durbin Model and Spillover Effect Decomposition
3.4.1. Green Technology Innovation, ACE and Digital Economy
3.4.2. Green Technology Innovation, APS and Digital Economy
3.5. Analysis of Grey Prediction
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Decoupling State | ΔACE | ΔAPS | et | |
---|---|---|---|---|
Optimal state | Strong decoupling | <0 | >0 | e < 0 |
Non-optimal state | Weak decoupling | >0 | >0 | 0 ≤ e < 0.8 |
Recessionary decoupling | <0 | <0 | e > 1.2 | |
Strong negative decoupling | >0 | <0 | e < 0 | |
Weak negative decoupling | <0 | <0 | 0 ≤ e < 0.8 | |
Expansion negative decoupling | >0 | >0 | e > 1.2 | |
Expansion connection | >0 | >0 | 0.8 ≤ e < 1.2 | |
Recessionary connection | <0 | <0 | 0.8 ≤ e < 1.2 |
First-Level Indicators | Secondary Indicators | Measure | Unit | Type | Weight Coefficient |
---|---|---|---|---|---|
Digital industrialization | Electronic information manufacturing level | Output of main products in electronic information manufacturing industry | 104 PCS | + | 0.121 |
Telecommunication service level | Income of total telecom business | 109 RMB | + | 0.084 | |
Internet development | Number of Internet broadband access ports | 104 PCS | + | 0.110 | |
Software and information technology service level | Income of Software business | 104 RMB | + | 0.076 | |
Industrial digitalization | Industrial Internet | Length of long-distance optical cable | 104 km | + | 0.109 |
Intelligent manufacturing | Technical market turnover | 109 RMB | + | 0.073 | |
Platform economy | E-commerce sales | 109 RMB | + | 0.122 | |
Digital logistics | Express delivery volume | 104 PCS | + | 0.106 | |
Digital governance | Digital public service | Number of automatic weather stations | PCS | + | 0.069 |
Data value | Geographic information data production | Surveying and mapping benchmark results | PCS | + | 0.129 |
Variable | Measure | Mean | Std. Deviation | Min | Max |
---|---|---|---|---|---|
lnACE | Total ACE | 5.535 | 1.616 | 2.440 | 12.321 |
lnAPS | Output of major crop products | 7.683 | 1.205 | 4.439 | 9.367 |
lnGTI | Number of green patent applications | 8.096 | 1.401 | 3.434 | 11.116 |
lnOPE | TIEPGVAP | 0.571 | 1.631 | −1.991 | 5.399 |
lnIGE | Intensity of government environmental expenditure | 4.817 | 0.625 | 3.055 | 6.617 |
lnR&D | Research and experimental development expenditure | 5.551 | 1.326 | 2.313 | 8.158 |
lnURB | Urban share of total population | 4.046 | 0.201 | 3.537 | 4.545 |
lnLAS | Employees’ number over the years | 7.507 | 0.819 | 4.644 | 8.785 |
lnDGE | Digital economy development level by VHSD | 0.131 | 0.097 | 0.007 | 0.681 |
Variables | I | E(I) | sd(I) | z | p-Value | |
---|---|---|---|---|---|---|
Moran’s I | lnACE | −0.094 | −0.003 | −4.587 | −2.604 | 0.000 |
lnAPS | −0.056 | −0.003 | 0.020 | −2.616 | 0.009 | |
lnGTI | 0.272 | −0.003 | 0.020 | 13.695 | 0.000 | |
Geary’s c | lnACE | 1.193 | 1.000 | 0.031 | 6.134 | 0.000 |
lnAPS | 1.047 | 1.000 | 0.023 | 2.003 | 0.045 | |
lnGTI | 0.697 | 1.000 | 0.023 | −13.359 | 0.000 |
Variable | lnDGE | Models | LM-Lag Test | Robust LM-Lag Test | LR Test | Wald Test | Hausman | LR Test | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Ind Fixed Effect | Time Fixed Effect | |||||||||||||||
Statistic | p-Value | Statistic | p-Value | Statistic | p-Value | Statistic | p-Value | Statistic | p-Value | Statistic | p-Value | Statistic | p-Value | |||
lnACE | No | SLM | 15.445 | 0.000 | 13.939 | 0.000 | 76.780 | 0.000 | 56.010 | 0.000 | 64.89 | 0.000 | 61.350 | 0.000 | 1867.48 | 0.000 |
SEM | 8.561 | 0.003 | 7.055 | 0.008 | 51.360 | 0.000 | 88.120 | 0.000 | ||||||||
Yes | SLM | 15.541 | 0.000 | 14.157 | 0.000 | 78.400 | 0.000 | 59.090 | 0.000 | 82.82 | 0.000 | 40.650 | 0.000 | 1867.53 | 0.000 | |
SEM | 8.155 | 0.004 | 6.772 | 0.009 | 54.350 | 0.000 | 90.090 | 0.000 | ||||||||
lnAPS | No | SLM | 0.034 | 0.853 | 3.224 | 0.073 | 25.730 | 0.001 | 24.030 | 0.001 | 46.26 | 0.000 | 19.280 | 0.037 | 1229.46 | 0.000 |
SEM | 2.375 | 0.123 | 5.565 | 0.018 | 23.480 | 0.001 | 27.160 | 0.000 | ||||||||
Yes | SLM | 0.012 | 0.912 | 3.568 | 0.059 | 26.520 | 0.002 | 24.800 | 0.003 | 175.98 | 0.000 | 19.950 | 0.030 | 1214.04 | 0.000 | |
SEM | 0.073 | 0.111 | 6.091 | 0.014 | 25.030 | 0.009 | 28.040 | 0.001 |
Variable | lnACE | lnACE | lnACE | lnACE | lnACE | lnACE |
---|---|---|---|---|---|---|
FE | FE | MLE | MLE | SDM | SDM | |
lnGTI | 0.144 *** (2.93) | 0.143 *** (2.92) | 0.148 *** (3.06) | 0.147 *** (3.05) | 0.136 *** (3.52) | 0.146 *** (3.73) |
(lnGTI)2 | −0.005 (−1.62) | −0.006 * (−1.75) | −0.005 * (−1.75) | −0.006 * (−1.88) | −0.007 *** (−2.83) | −0.007 *** (−3.04) |
lnGTI × lnDGE | — | −0.061 (−1.23) | — | −0.06 (−1.23) | — | −0.025 (−0.75) |
(lnGTI)2 × lnDGE | — | 0.006 (1.01) | — | 0.006 (1.00) | — | 0.003 (0.72) |
lnOPE | −0.03 (−1.57) | −0.034 * (−1.74) | −0.034 * (−1.79) | −0.037 ** (−1.97) | −0.031 ** (−2.37) | −0.031 ** (−2.30) |
lnIGE | −0.188 *** (−6.94) | −0.184 *** (−6.80) | −0.186 *** (−7.00) | −0.183 *** (−6.88) | −0.121 *** (−2.37) | −0.124 *** (−2.30) |
lnR&D | −0.028 (−0.71) | −0.022 (−0.57) | −0.022 (−0.56) | −0.016 (−0.42) | 0.088 ** (2.49) | 0.093 *** (2.60) |
lnURB | −0.066 (−0.42) | −0.081 (−0.51) | −0.097 (−0.63) | −0.111 (−0.72) | 0.477 *** (4.02) | 0.435 *** (3.59) |
lnLAS | 0.017 (0.34) | 0.01 (0.19) | 0.026 (0.53) | 0.019 (0.38) | 0.119 *** (3.13) | 0.116 *** (3.02) |
W × lnGTI | — | — | — | — | 0.414 ** (2.54) | 0.483 *** (2.70) |
W × (lnGTI)2 | — | — | — | — | −0.027 ** (−2.26) | −0.031 ** (−2.42) |
W × lnGTI × lnDGE | — | — | — | — | — | 0.214 (1.42) |
W × (lnGTI)2 × lnDGE | — | — | — | — | — | −0.023 (−1.20) |
W × lnOPE | — | — | — | — | −0.112 (−1.51) | −0.084 (−1.10) |
W × lnIGE | — | — | — | — | −0.004 (−0.03) | −0.022 (−0.18) |
W × lnR&D | — | — | — | — | −0.607 *** (−4.31) | −0.572 *** (−3.58) |
W × lnURB | — | — | — | — | −1.120 * (−1.67) | −1.241 * (−1.75) |
W × lnLAS | — | — | — | — | 1.174 *** (4.85) | 1.253 *** (5.07) |
rho | — | — | — | — | −0.612 ** (−2.53) | −0.594 ** (−2.44) |
_cons | 5.934 *** (8.59) | 6.051 *** (8.64) | 5.943 *** (8.03) | 6.055 *** (8.12) | — | — |
Variable | No lnDGE | Yes lnDGE | ||||
---|---|---|---|---|---|---|
Direct Effect | Indirect Effect | Total Effect | Direct Effect | Indirect Effect | Total Effect | |
lnGTI | 0.128 *** (3.29) | 0.213 ** (2.19) | 0.341 *** (3.38) | 0.137 *** (3.47) | 0.265 ** (2.33) | 0.402 *** (3.37) |
(lnGTI)2 | −0.006 ** (−2.56) | −0.014 * (−1.82) | −0.021 *** (−2.69) | −0.007 *** (−2.71) | −0.018 ** (−2.10) | −0.025 *** (−2.95) |
lnGTI × lnDGE | — | — | — | −0.028 (−0.84) | 0.158 (1.46) | 0.13 (1.27) |
(lnGTI)2 × lnDGE | — | — | — | 0.003 (0.81) | −0.017 (−1.29) | −0.014 (−1.09) |
lnOPE | −0.028 ** (−2.19) | −0.059 (−1.16) | −0.087 * (−1.66) | −0.029 ** (−2.22) | −0.043 (−0.80) | −0.072 (−1.27) |
lnIGE | −0.124*** (−6.65) | 0.039 (0.51) | −0.084 (−1.08) | −0.124 *** (−6.51) | 0.028 (0.36) | −0.096 (−1.16) |
lnR&D | 0.105 *** (3.05) | −0.435 *** (−3.45) | −0.330 *** (−2.70) | 0.108 *** (2.93) | −0.412 *** (−2.95) | −0.303 ** (−2.27) |
lnURB | 0.524 *** (4.16) | −0.948 ** (−2.08) | −0.424 (−0.97) | 0.471 *** (4.07) | −1.001 * (−1.91) | −0.53 (−1.06) |
lnLAS | 0.093 ** (2.41) | 0.730 *** (3.26) | 0.823 *** (3.57) | 0.090 ** (2.34) | 0.790 *** (3.39) | 0.880 *** (3.59) |
Variable | lnAPS | lnAPS | lnAPS | lnAPS | lnAPS | lnAPS |
---|---|---|---|---|---|---|
Fe | Fe | MLE | MLE | SDM | SDM | |
lnGTI | −0.086 * (−1.71) | −0.085 * (−1.69) | −0.077 (−1.55) | −0.077 (−1.54) | −0.07 (−1.34) | −0.077 (−1.45) |
(lnGTI)2 | 0.007 ** (2.27) | 0.008 ** (2.34) | 0.007 ** (2.09) | 0.007 ** (2.18) | 0.007 ** (2.20) | 0.008 ** (2.36) |
lnGTI × lnDGE | — | 0.038 (0.75) | — | 0.41 (0.82) | — | 0.018 (0.38) |
(lnGTI)2 × lnDGE | — | −0.003 (−0.54) | — | −0.004 (−0.62) | — | −0.001 (−0.27) |
lnOPE | −0.118 *** (−5.98) | −0.115 *** (−5.77) | −0.123 *** (−6.35) | −0.120 *** (−6.17) | −0.124 *** (−6.74) | −0.124 *** (−6.69) |
lnIGE | −0.116 *** (−4.19) | −0.120 *** (−4.29) | −0.115 *** (−4.20) | −0.118 *** (−4.32) | −0.118 *** (−6.74) | −0.117 *** (−6.69) |
lnR&D | −0.082 ** (−2.04) | −0.088 ** (−2.16) | −0.070 * (−1.75) | −0.075 * (−1.88) | −0.055 (−1.11) | −0.063 (−1.28) |
lnURB | 1.024 *** (6.31) | 1.034 *** (6.36) | 0.959 *** (5.98) | 0.971 *** (6.07) | 1.120 *** (6.79) | 1.151 *** (6.82) |
lnLAS | −0.150 *** (−2.95) | −0.147 *** (−2.84) | −0.129 ** (−2.55) | −0.125 ** (−2.45) | −0.036 (−0.68) | −0.037 (−0.69) |
W × lnGTI | — | — | — | — | 0.055 (0.25) | −0.041 (−0.17) |
W × (lnGTI)2 | — | — | — | — | −0.028 * (−1.73) | −0.021 (−1.19) |
W × lnGTI × lnDGE | — | — | — | — | — | −0.185 (−0.88) |
W × (lnGTI)2 × lnDGE | — | — | — | — | — | 0.027 (1.01) |
W × lnOPE | — | — | — | — | −0.073 (−0.72) | −0.099 (−0.94) |
W × lnIGE | — | — | — | — | 0.109 (0.67) | 0.103 (0.64) |
W × lnR&D | — | — | — | — | −0.540 *** (−2.96) | −0.657 *** (−3.05) |
W × lnURB | — | — | — | — | 0.161 (0.17) | 0.328 (0.33) |
W × lnLAS | — | — | — | — | 0.302 (0.91) | 0.258 (0.76) |
rho | — | — | — | — | −0.744 *** (−3.10) | −0.739 *** (−3.06) |
_cons | 5.953 *** (8.38) | 5.892 *** (8.17) | 5.949 *** (8.03) | 5.880 *** (7.85) | — | — |
Variable | No lnDGE | Yes lnDGE | ||||
---|---|---|---|---|---|---|
Direct Effect | Indirect Effect | Total Effect | Direct Effect | Indirect Effect | Total Effect | |
lnGTI | −0.071 (−1.31) | 0.063 (0.53) | −0.008 (−0.07) | −0.076 (−1.38) | 0.021 (0.14) | −0.055 (−0.36) |
(lnGTI)2 | 0.008 ** (2.33) | −0.020 * (−1.94) | −0.012 (−1.18) | 0.009 ** (2.44) | −0.017 (−1.55) | −0.009 (−0.80) |
lnGTI × lnDGE | — | — | — | 0.029 (0.60) | −0.113 (−0.83) | −0.084 (−0.67) |
(lnGTI)2 × lnDGE | — | — | — | −0.003 (−0.52) | 0.016 (0.94) | 0.013 (0.81) |
lnOPE | −0.123 *** (−6.91) | 0.015 (0.24) | −0.108 * (−1.78) | −0.124 *** (−6.80) | −0.002 (−0.03) | −0.126 * (−1.87) |
lnIGE | −0.124 *** (−4.81) | 0.114 (1.21) | −0.01 (−0.11) | −0.121 *** (−4.52) | 0.113 (1.21) | −0.008 (−0.09) |
lnR&D | −0.04 (−0.82) | −0.301 ** (−2.48) | −0.340 *** (−3.05) | −0.045 (−0.85) | −0.362 ** (−2.51) | −0.406 *** (−3.06) |
lnURB | 1.156 *** (6.54) | −0.436 (−0.79) | 0.72 (1.43) | 1.165 *** (7.11) | −0.331 (−0.54) | 0.834 (1.47) |
lnLAS | −0.043 (−0.80) | 0.205 (0.98) | 0.162 (0.76) | −0.041 (−0.80) | 0.172 (0.85) | 0.132 (0.63) |
Decoupling State | Year | Beijing | Inner Mongolia | Liaoning | Jilin | Heilongjiang | Shanghai | Anhui | Jiangxi | Hubei | Yunnan | Ningxia |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Relatively short-term actual state | 2016 | Weak negative | Expansion negative | Recessionary | Weak | Weak negative | Weak negative | Weak negative | Weak negative | Weak negative | Strong negative | Strong negative |
2017 | Recessionary connection | Strong | Strong | Strong | Strong | Recessionary connection | Strong | Strong | Strong | Weak negative | Weak negative | |
2018 | Weak negative | Recessionary connection | Weak negative | Strong | Recessionary | Strong | Strong | |||||
2019 | Recessionary | Strong | Strong | Recessionary | Recessionary connection | Recessionary | Recessionary | Weak | ||||
2020 | Weak negative | Recessionary | Recessionary connection | Weak negative | Strong | Strong negative | Recessionary | Strong | Strong | Strong | Strong negative | |
Relatively short-term forecast state | 2016 | Expansion negative | Recessionary | Weak | Weak negative | Weak negative | Weak negative | Weak negative | Weak negative | Strong negative | ||
2017 | Weak | Strong | Weak | Expansion connection | Weak | Weak | Weak | |||||
2018 | Recessionary connection | Strong | Recessionary connection | Strong | Recessionary | Strong | Recessionary | Strong | Strong | |||
2019 | Recessionary | |||||||||||
2020 | ||||||||||||
2021 | ||||||||||||
2022 | ||||||||||||
2023 | ||||||||||||
2024 | ||||||||||||
2025 | ||||||||||||
Relatively long-term actual state | 2016 | Weak negative | Expansion negative | Weak | Expansion connection | Expansion connection | Weak negative | Weak | Weak | Strong | Expansion negative | Weak |
2017 | Weak | Strong | Weak | Weak | Strong | Strong negative | ||||||
2018 | Expansion connection | Recessionary connection | Strong | Weak | ||||||||
2019 | Weak | Weak | ||||||||||
2020 | Weak negative | Strong negative | ||||||||||
Relatively long-term forecast state | 2016 | Expansion negative | Weak | Expansion connection | Expansion connection | Weak | Weak | Expansion negative | Weak | |||
2017 | Weak | Strong | Weak | Weak | Expansion connection | Weak | Strong negative | Strong negative | ||||
2018 | Recessionary connection | Strong | Strong | Strong | Strong | Strong | Strong | Strong | Recessionary | Weak negative | ||
2019 | Recessionary | Recessionary connection | ||||||||||
2020 | Strong | Recessionary | ||||||||||
2021 | Recessionary | |||||||||||
2022 | ||||||||||||
2023 | ||||||||||||
2024 | ||||||||||||
2025 | Strong |
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Zhang, L.; Chen, J.; Dinis, F.; Wei, S.; Cai, C. Decoupling Effect, Driving Factors and Prediction Analysis of Agricultural Carbon Emission Reduction and Product Supply Guarantee in China. Sustainability 2022, 14, 16725. https://doi.org/10.3390/su142416725
Zhang L, Chen J, Dinis F, Wei S, Cai C. Decoupling Effect, Driving Factors and Prediction Analysis of Agricultural Carbon Emission Reduction and Product Supply Guarantee in China. Sustainability. 2022; 14(24):16725. https://doi.org/10.3390/su142416725
Chicago/Turabian StyleZhang, Lin, Jinyan Chen, Faustino Dinis, Sha Wei, and Chengzhi Cai. 2022. "Decoupling Effect, Driving Factors and Prediction Analysis of Agricultural Carbon Emission Reduction and Product Supply Guarantee in China" Sustainability 14, no. 24: 16725. https://doi.org/10.3390/su142416725
APA StyleZhang, L., Chen, J., Dinis, F., Wei, S., & Cai, C. (2022). Decoupling Effect, Driving Factors and Prediction Analysis of Agricultural Carbon Emission Reduction and Product Supply Guarantee in China. Sustainability, 14(24), 16725. https://doi.org/10.3390/su142416725