China’s Eco-Efficiency: Regional Differences and Influencing Factors Based on a Spatial Panel Data Approach
Abstract
:1. Introduction
2. Literature Review
3. Methodology
3.1. The EBM DEA Model with Undesirable Outputs
3.2. Global Moran’s I
3.3. Spatial Durbin Model
4. Data Source and Indicator Selection
4.1. Indicators of EE Evaluation
4.2. Factors Influencing EE
5. Empirical Analysis
5.1. Overall Characteristics of Chinese EE
5.2. Regional Characteristics of EE
5.2.1. Eastern Area
5.2.2. Central Area
5.2.3. Western Area
5.2.4. Northeastern Area
5.3. Regression Results and Analysis
5.3.1. Smulticollinearity Test
5.3.2. Spatial Autocorrelation Test
5.3.3. LM and Robust LM Tests
5.3.4. Wald and LR Tests
β5ULi,t + β6PDi,t +θ1WDELi,t +θ2WISi,t + θ3W*FTDi,t +θ4WTPi,t +
θ5WULi,t +θ6WPDi,t +εi,t εi,t ~ N(0, σ2i,t In),
5.3.5. Analysis Results
6. Conclusions and Policy Suggestions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Author | Methodology | Objects and Period | Variables |
---|---|---|---|
Fan et al. [40] | CCR and BCC DEA models | The eco-efficiency levels of 40 Chinese industrial parks in 2012 | Input: Land, Energy, Water Desirable output: Industrial value added Undesirable output: Wastewater, Solid waste, COD, SO2 |
Pai et al. [41] | CCR and BCC DEA models | Th eco-efficiencies of 60 industrial parks in Taiwan | Input: Site area, Labor force, Electricity, Water, Waste discharge, Airborne, Particles Output: The overall operating income |
Moutinho et al. [42] | BCC DEA model | The eco-efficiency of the 16 Latin American countries from 1994 to 2013 | Input: Population density, labor productivity, Energy, Renewable energy, Gross capital formation productivity Output: The inverse ratio of carbon intensity |
Rybaczewska-Bła˙ zejowsk and Gierulski [43] | Life cycle assessment (LCA) and BCC DEA model | Th eco-efficiencies of agricultural production in 28 member states of the European Union in 2015 | Input: Labor, Capital, Energy Desirable output: GDP Undesirable output: SO2 |
Shah et al. [44] | CCR and BCC DEA models | The eco-efficiency at the industrial park/complex level of Ulsan metropolis and Korea in 2005, and 2010, and 2015 | Input: Land, Labor force, Energy Output: Gross output |
Peng et al. [47] | The SBM DEA model with undesirable outputs | The eco-efficiency of the Huangshan National Park in China from 1981 to 2014 | Input: Average wage level of employees, New fixed asset investment, Energy, Water, Desirable output: Per capita tourism income Undesirable output: Garbage, Sewage, Waste gas |
Ning et al. [48] | The SBM DEA model with undesirable outputs | The eco-efficiency of state-owned forestry enterprises in Northeast China from 2003 to 2015 | Input: Labor, Capital, Land Desirable output: Total output, Sale Undesirable output: Effluent, Exhaust, Solid-waste |
Zheng et al. [49] | The SBM DEA model with undesirable outputs | The eco-efficiency of the Chinese 31 provinces from 2000 to 2015 | Input: Water footprint; Labor force; Capital, Cost of resource and environment, Land Desirable output: GDP Undesirable output: Gray water footprint, Environmental pollutants |
Wang et al. [50] | The SBM DEA model with undesirable outputs | The eco-efficiency of regional tourism in Chinese 31 provinces from 1997 to 2016 | Input: Labor, Capital, Water, Energy Desirable output: Revenue from tourism Undesirable output: Tourism effluent discharge Tourism waste discharge Tourism SO2, Tourism CO2, |
Yang et al. [16] | The EBM DEA model | The ecological energy efficiency of in Chinese 30 provinces from 2007 to 2015 | Input: Labor, Capital, Energy, SO2, NOX Desirable output: GDP |
Chen et al. [51] | The EBM DEA model with undesirable outputs | The ecological efficiency of in Chinese 259 cities from 2007 to 2016 | Input: Labor, Capital, Energy, Water, Land Desirable output: GDP Undesirable output: Industrial discharged wastewater, Industrial sulfur dioxide emission, Industrial soot (dust) emission |
Primary Indicators | Secondary Indicators | Mean | Std. Dev | Min | Max |
---|---|---|---|---|---|
Inputs | Capital stock (unit: 108 yuan) | 68,357.9 | 46,755.2 | 5832 | 231,280 |
Labor force (unit: 104) | 2666.7 | 1744.7 | 301 | 6767 | |
Total energy consumption (unit: 104 tons of SCE) | 13,624.5 | 8157.9 | 1135 | 38,899 | |
Total water consumption (unit: 108 L) | 201 | 142 | 22.3 | 591.3 | |
Urban construction land (unit: sq.km) | 1567.1 | 1078.1 | 109 | 5577 | |
Desired outcomes | GDP (unit: 108 yuan) | 18,559.3 | 15,297.9 | 1019 | 80,956 |
Undesired outcomes | CO2 emissions (unit: 106 tons) | 300.5 | 192.2 | 25 | 842 |
SO2 emissions (unit: 104 tons) | 63 | 40.7 | 1.43 | 182.7 | |
Smoke and dust emissions (unit: 104 tons) | 58.9 | 43. | 5.75 | 198.3 | |
COD emissions (unit: 104 tons) | 43.4 | 31.1 | 1.47 | 179.8 | |
Ammonia nitrogen (unit: 104 tons) | 6.25 | 4.51 | 0.56 | 23.09 |
Explanatory Variable | Variables’ Definition and Unit | Key References | Pre-Judgment |
---|---|---|---|
Economic development level (EDL) | GDP per capita (104 RMB) | [55,58,79,80] | Positive |
Industrial structure (IS) | The proportion of the added value of the tertiary industry to provincial GDP (%) | [55,81,82] | Positive |
Foreign trade dependence(FTD) | The proportion of the total import and export trade to provincial GDP (%) | [54,79,80,83] | Unknown |
Technological progress (TP) | Proportion of R&D expenditure to provincial GDP (%) | [58,80,84] | Positive |
Urbanization level (UL) | The proportion of city population in total population (%) | [85,86] | Negative |
Population density(PD) | Ratio of the total regional population to regional area (person/sq.km) | [62,84,87,88] | Unknown |
Regions | Provinces | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | Mean |
---|---|---|---|---|---|---|---|---|---|---|---|---|
East | Beijing | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Tianjing | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
Hebei | 0.751 | 0.696 | 0.732 | 0.738 | 0.69 | 0.666 | 0.634 | 0.643 | 0.641 | 0.593 | 0.678 | |
Shanghai | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
Jiangsu | 0.889 | 0.884 | 0.893 | 0.855 | 0.811 | 0.789 | 0.78 | 0.761 | 0.773 | 0.75 | 0.818 | |
Zhejiang | 1 | 1 | 1 | 0.905 | 0.874 | 0.814 | 0.805 | 0.861 | 0.893 | 0.831 | 0.898 | |
Fujian | 1 | 1 | 1 | 0.852 | 0.792 | 0.771 | 0.755 | 0.743 | 0.778 | 0.7 | 0.839 | |
Shandong | 0.772 | 0.759 | 0.8 | 0.783 | 0.734 | 0.738 | 0.697 | 0.695 | 0.713 | 0.682 | 0.737 | |
Guangdong | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
Hainan | 0.569 | 0.645 | 0.607 | 0.635 | 0.631 | 0.581 | 0.589 | 0.509 | 0.558 | 0.56 | 0.588 | |
Mean | 0.898 | 0.898 | 0.903 | 0.877 | 0.853 | 0.836 | 0.826 | 0.821 | 0.836 | 0.812 | 0.898 | |
Central | Shanxi | 0.66 | 0.619 | 0.633 | 0.662 | 0.614 | 0.597 | 0.57 | 0.564 | 0.546 | 0.517 | 0.598 |
Anhui | 0.535 | 0.543 | 0.591 | 0.62 | 0.605 | 0.597 | 0.569 | 0.582 | 0.591 | 0.558 | 0.579 | |
Jiangxi | 0.623 | 0.624 | 0.659 | 0.684 | 0.649 | 0.632 | 0.622 | 0.612 | 0.619 | 0.581 | 0.631 | |
Henan | 0.703 | 0.686 | 0.706 | 0.708 | 0.662 | 0.644 | 0.614 | 0.622 | 0.652 | 0.626 | 0.662 | |
Hubei | 0.565 | 0.59 | 0.599 | 0.635 | 0.613 | 0.625 | 0.574 | 0.618 | 0.626 | 0.563 | 0.601 | |
Hunan | 0.657 | 0.625 | 0.69 | 0.736 | 0.713 | 0.706 | 0.68 | 0.715 | 1 | 0.661 | 0.718 | |
Mean | 0.624 | 0.614 | 0.646 | 0.674 | 0.643 | 0.633 | 0.605 | 0.619 | 0.672 | 0.584 | 0.624 | |
West | Inner Mongolia | 0.666 | 0.665 | 0.661 | 0.674 | 0.639 | 0.632 | 0.604 | 0.637 | 0.699 | 0.658 | 0.653 |
Guangxi | 0.55 | 0.607 | 0.639 | 0.664 | 0.601 | 0.582 | 0.567 | 0.566 | 0.565 | 0.525 | 0.587 | |
Chongqing | 0.629 | 0.616 | 0.649 | 0.68 | 0.702 | 0.705 | 0.672 | 0.666 | 0.678 | 0.647 | 0.664 | |
Sichuan | 0.655 | 0.637 | 0.672 | 0.703 | 0.672 | 0.661 | 0.63 | 0.627 | 0.616 | 0.58 | 0.645 | |
Guizhou | 0.551 | 0.549 | 0.619 | 0.622 | 0.588 | 0.573 | 0.554 | 0.544 | 0.527 | 0.469 | 0.56 | |
Yunnan | 0.598 | 0.598 | 0.623 | 0.617 | 0.6 | 0.604 | 0.564 | 0.555 | 0.538 | 0.491 | 0.579 | |
Shaanxi | 0.683 | 0.782 | 1 | 1 | 0.718 | 0.682 | 0.654 | 0.664 | 0.682 | 0.621 | 0.749 | |
Gansu | 0.508 | 0.51 | 0.546 | 0.557 | 0.541 | 0.542 | 0.512 | 0.523 | 0.524 | 0.507 | 0.527 | |
Qinghai | 0.604 | 0.6 | 0.633 | 0.654 | 0.599 | 0.538 | 0.499 | 0.49 | 0.502 | 0.475 | 0.559 | |
Ningxia | 0.364 | 0.368 | 0.413 | 0.413 | 0.397 | 0.391 | 0.377 | 0.366 | 0.364 | 0.343 | 0.38 | |
Xinjiang | 0.472 | 0.477 | 0.499 | 0.502 | 0.472 | 0.452 | 0.424 | 0.422 | 0.423 | 0.392 | 0.454 | |
Mean | 0.571 | 0.583 | 0.632 | 0.644 | 0.594 | 0.578 | 0.551 | 0.551 | 0.556 | 0.519 | 0.571 | |
Northeast | Liaoning | 0.565 | 0.578 | 0.615 | 0.639 | 0.618 | 0.605 | 0.59 | 0.597 | 0.592 | 0.55 | 0.595 |
Jilin | 0.51 | 0.511 | 0.541 | 0.539 | 0.535 | 0.55 | 0.54 | 0.539 | 0.552 | 0.526 | 0.534 | |
Heilongjiang | 0.552 | 0.561 | 0.607 | 0.628 | 0.605 | 0.591 | 0.559 | 0.56 | 0.571 | 0.544 | 0.578 | |
Mean | 0.542 | 0.55 | 0.588 | 0.602 | 0.586 | 0.582 | 0.563 | 0.565 | 0.572 | 0.54 | 0.542 | |
Average EEs in Chinese provinces | 0.688 | 0.691 | 0.721 | 0.724 | 0.689 | 0.676 | 0.654 | 0.656 | 0.674 | 0.632 | 0.680 |
InEE | InDEL | InIS | InFTD | InTP | InUL | InPD | |
---|---|---|---|---|---|---|---|
InEE | 1 | ||||||
InDEL | 0.5409 *** | 1 | |||||
InIS | 0.3723 *** | 0.5964 *** | 1 | ||||
InFTD | 0.7037 *** | 0.5694 *** | 0.5214 *** | 1 | |||
InTP | 0.6892 *** | 0.6621 *** | 0.5329 *** | 0.6349 *** | 1 | ||
InUL | 0.5991 *** | 0.9122 *** | 0.6726 *** | 0.7089 *** | 0.7090*** | 1 | |
InPD | 0.6723 *** | 0.4279 *** | 0.4240 *** | 0.6707 *** | 0.7078*** | 0.5293 *** | 1 |
InDEL | InIS | InFTD | InTP | InUL | InPD | Mean VIF | |
---|---|---|---|---|---|---|---|
VIF | 6.63 | 1.86 | 2.80 | 3.01 | 9.66 | 2.51 | 4.41 |
1/VIF | 0.150881 | 0.537701 | 0.357327 | 0.331950 | 0.103474 | 0.398968 |
Year | Moran’s I | Z-Score | p-Value |
---|---|---|---|
2008 | 0.423 *** | 3.696 | 0.000 |
2009 | 0.417 *** | 3.653 | 0.000 |
2010 | 0.299 *** | 2.682 | 0.007 |
2011 | 0.257 ** | 2.364 | 0.018 |
2012 | 0.381 *** | 3.406 | 0.001 |
2013 | 0.343 *** | 3.102 | 0.002 |
2014 | 0.380 *** | 3.411 | 0.001 |
2015 | 0.318 *** | 2.890 | 0.004 |
2016 | 0.285 ** | 2.591 | 0.010 |
2017 | 0.327 *** | 2.977 | 0.003 |
Spatial Error: | Spatial Lag | |
---|---|---|
Moran’s I | 3.707 *** | |
Lagrange multiplier | 189.649 *** | 50.831 *** |
Robust Lagrange multiplier | 139.848 *** | 1.030 |
Fixed Effects | Random Effects | |
---|---|---|
Wald test spatial lag | 501.84 *** | 127.34 *** |
LR test spatial lag | 134.89 *** | 134.89 *** |
Wald test spatial error | 22.27 *** | 7.13 *** |
LR test spatial error | 30.65 *** | 30.65 *** |
Spatial Fixed-Effects | Time Fixed-Effects | Spatial and Time Fixed-Effects | |
---|---|---|---|
InDEL | 0.397 *** | 0.053 | 0.402 *** |
InIS | 0.091 ** | −0.200 *** | 0.092 |
InFTD | 0.026 * | 0.124 *** | 0.027 * |
InTP | 0.080 * | 0.078 | 0.080 * |
InUL | −0.105 | 0.125 | −0.127 |
InPD | −0.046 *** | −0.040 * | −0.044 *** |
WInDEL | −0.306 *** | −0.064 | −0.259 |
WInIS | −0.008 | 0.141 | −0.003 *** |
WInFTD | −0.024 | −0.095 *** | −0.041 |
WInTP | −0.067 | −0.037 | −0.045 |
WInUL | −0.046 | −0.035 | −0.012 |
WInPD | 0.069 *** | 0.042 | 0.055 |
Spatial rho | 0.772 *** | 0.684 *** | 0.701 *** |
Variance sigma2_e | 0.005 | 0.010 | 0.005 |
R-squared | 0.775 | 0.640 | 0.672 |
Log-likelihood | 335.771 | 249.772 | 338.962 |
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Zeng, L. China’s Eco-Efficiency: Regional Differences and Influencing Factors Based on a Spatial Panel Data Approach. Sustainability 2021, 13, 3143. https://doi.org/10.3390/su13063143
Zeng L. China’s Eco-Efficiency: Regional Differences and Influencing Factors Based on a Spatial Panel Data Approach. Sustainability. 2021; 13(6):3143. https://doi.org/10.3390/su13063143
Chicago/Turabian StyleZeng, Liangen. 2021. "China’s Eco-Efficiency: Regional Differences and Influencing Factors Based on a Spatial Panel Data Approach" Sustainability 13, no. 6: 3143. https://doi.org/10.3390/su13063143
APA StyleZeng, L. (2021). China’s Eco-Efficiency: Regional Differences and Influencing Factors Based on a Spatial Panel Data Approach. Sustainability, 13(6), 3143. https://doi.org/10.3390/su13063143