Spatio-Temporal Urban Land Green Use Efficiency under Carbon Emission Constraints in the Yellow River Basin, China
Abstract
:1. Introduction
2. Literature Review
3. Theoretical Analysis
3.1. Concept Definition
3.1.1. Green Development
3.1.2. Urban Land
3.1.3. ULGUE
3.2. Theoretical Compendium
3.2.1. Land Intensive Use Theory
3.2.2. Efficiency Theory
3.2.3. Location Theory
3.3. Research Framework
4. Materials and Methods
4.1. Study Area
4.2. Super-SBM Model
4.3. Kernel Density Estimation
4.4. Spatial Autocorrelation Model
4.4.1. Global Moran’s Index
4.4.2. Local Moran’s Index
4.5. Spatial Durbin Model (SDM)
4.6. Indicator Selection
4.6.1. Indicators Measuring ULGUE
- (1)
- Input indicators: Land, labor, and capital refer to three significant factors in production. With reference to the existing studies, the area of the built-up area was selected to characterize land input [83]. Beyond that, the total number of unit employees and private and self-employed workers in municipal districts at the end of the year was selected to characterize the labor input. Besides, the amount of urban capital stock characterizes the capital input. In this study, the perpetual inventory method was used to account for the amount of investment in the urban fixed assets with reference to Zhang et al., and the depreciation rate was set to be 9.6% [84]. Moreover, the fixed asset price index was used for each province to convert prices by the base period.
- (2)
- Desirable output indicators: Based on the definition of ULGUE, this paper has set the expected output indexes from three perspectives: economic benefits, social benefits, and environmental benefits. To be specific, we used the GDP of secondary and tertiary industries in municipal districts as economic benefits and converted it into the constant price of 2004 using the GDP indices of different provinces [85,86]. Both urban employee salaries and total retail sales of social consumer goods, which reflect the social benefits, were treated to comparable numbers [87]. In addition, the area of parks and green spaces had been selected to evaluate the desirable output of environmental benefits [38].
- (3)
- Undesirable output indicators: In this paper, the undesirable outputs were set to two aspects, namely the industrial pollutants and carbon emissions from residential and secondary, and tertiary industries. Normally, industrial sulfur dioxide emissions, industrial wastewater emissions, and industrial waste gas emissions were chosen to measure the undesirable outputs for efficiency studies [88]. In the case of a certain number of DMUs, too many output indicators of the DEA model will affect the accuracy of the results, and the units of all three industrial pollutants are different [60]. This study used the entropy method to synthesize the “three wastes” into an industrial pollution index, together with energy consumption and carbon emissions as two undesirable outputs. Furthermore, the carbon emission data used in this paper came from the research results of Shan et al., which have been widely applied in carbon emission accounting studies [19,89,90,91]. Notably, both industrial pollutants and carbon emissions are citywide statistical caliber. Therefore, this paper not only discounted the industrial pollution index by the proportion of the total industrial output value of the municipal district to the total industrial output value of the city but also discounted the carbon emissions by the proportion of the GDP of the municipal district to the GDP of the city. All the indicators and explanations are shown in Table 1.
4.6.2. Influencing Factors of ULGUE
4.7. Data Source
5. Results
5.1. Time Series Variation Characteristics of ULGUE
5.2. Evolutionary Features of ULGUE
5.3. Spatial Distribution Characteristics of ULGUE
5.4. Regression Results of the SDM Model
5.5. Robustness Test
5.5.1. Independent Variable Replacement
5.5.2. Replacement of the Spatial Weight Matrix
5.6. Heterogeneity Test
6. Discussion
6.1. ULGUE Variations in the YRB
6.2. Paradigm of Low Carbon Development
6.3. Policy Implications for Improving ULGUE
- (1)
- Controlling the land supply amount, adhering to the urban development boundary, and improving ULGUE within the city’s built-up areas. The government should consider factors such as the carrying capacity of resources and the environment and the suitability of land space development. In addition, the economic development patterns should be based on local conditions. Meanwhile, urban development mode driven by the expansion of construction land together with the “pie” type of urban development should be strictly forbidden. It is also essential to promote the structural reform on the supply side of urban land and change from the development mode of traditional urban space expansion land to the optimization mode of urban land space layout. Moreover, upstream and midstream cities need to enhance the construction of green space facilities to increase the expected output of land. Besides, the downstream cities should promote the development of the real economy and reduce the real estate economic bubble with consideration of the economic development level.
- (2)
- Accelerating the green manufacturing industry, with the green sustainable development concept as the guidance. Industrial structure optimization and moderate intensification are encouraged to provide the ecological foundation for the green sustainable and healthy development of cities and livable cities. The long-standing development mode in the YRB has resulted in a large proportion of traditional manufacturing and resource-based industries in the industrial structure. At the same time, there is a low proportion of high-tech, advanced manufacturing, and modern service industries as well as a severe phenomenon of industrial homogeneity. It is necessary to rely on industrial transfer and industrial structure upgrading to promote the advanced manufacturing industry. With energy conservation and emission reduction as the policy guidance, the government should not only accelerate the implementation of peak planning and action plans for major cities and energy carbon-emitting industries, but also incorporate the implementation of carbon-emission control and air pollutant emission reduction into the central environmental protection inspectors, local party, and government ecological and environmental leadership of the responsibility audit system.
- (3)
- Actively promoting the synergistic development among cities and city agglomerations in the YRB in the context of regional integrated development. Departments should take the core cities and metropolitan areas as the entry point, pull the surrounding cities with their comprehensive advantages, further link the surrounding node cities to form a radiation circle, and gradually drive the development of the small and medium-sized cities. Since there is an increasing spatial spillover effect of ULGUE, the governmental departments should actively review the spatial interaction between local cities and neighboring cities and strengthen the role of linkage control between cities in land urbanization. Apart from that, the ULGUEs of the Lanzhou-Xining Urban Agglomeration and Hohhot-Baotou-Ordos-Yulin Urban Agglomeration is relatively high, while the Guanzhong Plain Urban Agglomeration is at a lower efficiency level. It is of great significance to use the positive spillover effect of government financial support, industrial restructuring, and infrastructure construction to promote the positive transmission of ULGUE in cities.
6.4. Limitations and Future Prospects
7. Conclusions
- (1)
- The temporal change patterns of ULGUE in the Yellow River Basin exhibited a “U”-shaped feature. Namely, it declined first and then increased with the development of the cities. The ULGUE in the YRB decreased from 2004 to 2010, while rising to a higher level from 2010 to 2017, which indicates the possible existence of an environmental Kuznets curve in the development of the urban economy. The average efficiency in the upstream area was the highest, followed by the downstream area and the midstream area (lowest). Although most upstream cities have a pattern of “low development level and low emission” pattern, the downstream cities have an agglomeration characteristic of “high development level and high emission”. There are many resource-based cities in the midstream, with significant differences among cities. This provides empirical support for the recognition of ULGUE in multiple types of cities.
- (2)
- ULGUE in the upstream, midstream, and downstream areas presented distinctive spatial non-equilibrium characteristics. Especially, the bi-polar pattern of ULGUE had been present during the study period. The spatial autocorrelation of ULGUE during 2004–2013 was insignificant, but a significant positive spatial correlation increased yearly after 2013, indicating that the positive inter-regional synergy feature is becoming more decisive annually. Meanwhile, the natural resource endowments can also influence the spatial distribution of ULGUE. What’s more, the Local Moran’s index displayed a “north high and south low” distribution. The Inner Mongolia Plateau and Loess Plateau regions were the high-value areas for ULGUE, while the Guanzhong Plain was a low-value aggregation area. That is to say, areas with low topographic relief and low elevation are easy for urban construction, but not easy for intensive use of land resources. In this regard, more scientific urban planning and policy support are needed.
- (3)
- Under the spatial spillover effect, the influencing factors of ULGUE showed a complex mechanism. From the perspective of a single city, the regional economic development significantly improved the ULGUE, while the fiscal expenditure intensity, employment structure, environmental regulation, and development intensity significantly negatively affected the ULGUE. Considering the decomposition of direct effects, indirect effects, and total effects, the fiscal expenditure intensity, environmental regulation, and industrial structure (high percentage of tertiary industry) had a significant positive spatial spillover effect. Apart from that, the total effects of fiscal expenditure intensity, environmental regulation, and industrial structure were all positively significant. Besides, the midstream and downstream were more prone to economic siphoning impacts, resulting in inefficient ULGUE of the neighboring cities. In short, reasonable policy regulations are needed to adjust the ULGUE of a region.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Indicator | Variable Type | Variable Explanation | Unit |
---|---|---|---|
Input | Land | Urban built-up area | km2 |
Labor | Total number of urban employees | 10 thousand person | |
Capital | Urban capital stock | billion CNY | |
Desirable Output | Economic benefits | Secondary and tertiary industry GDP in municipal districts | billion CNY |
Social benefits | Total retail sales of social consumer goods | 10 thousand CNY | |
Urban employee salary | CNY | ||
Environmental benefits | Area of parks and green spaces | hm2 | |
Undesirable Output | Industrial Pollution | Composite index synthesized by the entropy method including industrial wastewater, SO2, and soot emissions | / |
Carbon Emission | Carbon emissions from urban energy consumption | million ton |
Variable Name | Variable Content | Variable Explanation | Unit |
---|---|---|---|
pgdp | Economic development level | Gross Domestic Product per capita | CNY/person |
is | Industrial structure | Secondary industry output value/total GDP | % |
pd | Population density | Year-end population/area of the municipal district | Person/km2 |
ge | Governmental expenditure intensity | Public finance expenditure/total GDP | % |
es | Employment structure | Number of self-employed and private employees/total number of employees | % |
road | Infrastructure Development | Road area per capita | m2/person |
er | Environmental regulation | A composite index of industrial solid waste utilization rate, domestic sewage treatment rate, and domestic waste harmless treatment rate generated by the entropy method | / |
eip | Development intensity | Real estate development investment completion amount/city area | 10 thousand CNY/km2 |
Year | Moran’s I | Z-Score | p-Value | Year | Moran’s I | Z-Score | p-Value |
---|---|---|---|---|---|---|---|
2004 | −0.001 | 0.821 | 0.411 | 2011 | 0.006 | 1.127 | 0.260 |
2005 | 0.002 | 0.945 | 0.345 | 2012 | 0.003 | 1.003 | 0.316 |
2006 | −0.023 | −0.223 | 0.823 | 2013 | 0.024 | 2.027 | 0.043 |
2007 | −0.023 | −0.237 | 0.812 | 2014 | 0.030 | 1.729 | 0.084 |
2008 | −0.014 | 0.175 | 0.861 | 2015 | 0.040 | 2.749 | 0.006 |
2009 | 0.005 | 1.097 | 0.272 | 2016 | 0.055 | 3.426 | 0.001 |
2010 | 0.009 | 1.281 | 0.200 | 2017 | 0.064 | 3.882 | 0.000 |
Test | Test Statistics | p-Value |
---|---|---|
LM-Lag | 3.018 | 0.082 |
Robust LM-Lag | 7.691 | 0.006 |
LM-Err | 9.185 | 0.000 |
Robust LM-Err | 6.290 | 0.012 |
Wald test for SAR | 34.160 | 0.000 |
Wald test for SEM | 36.520 | 0.000 |
LR test for both and spatial fixed | 64.680 | 0.000 |
LR test for both and time fixed | 589.940 | 0.000 |
LR-SDM-SAR | 33.320 | 0.000 |
LR-SDM-SEM | 35.570 | 0.000 |
Hausman | 13.500 | 0.096 |
Type | (1) SDM | (2) SDM | (3) SDM |
---|---|---|---|
(Spatial Fixed) | (Time Fixed) | (Time-Spatial Fixed) | |
Main | |||
lnpgdp | 0.221 *** (4.65) | 0.211 *** (8.82) | 0.227 *** (4.92) |
lnge | −0.074 ** (−2.87) | 0.084 *** (3.82) | −0.047 (−1.88) |
lnis | 0.033 (0.57) | −0.281 *** (−8.15) | 0.014 (0.25) |
lnes | −0.073 ** (−3.14) | −0.142 *** (−5.86) | −0.071 ** (−3.09) |
lnroad | −0.019 (−0.82) | 0.024 (1.20) | −0.018 (−0.80) |
lner | −0.126 ** (−3.06) | −0.178 *** (−3.97) | −0.105 ** (−2.60) |
lnpd | −0.016 (−0.38) | −0.063 ** (−2.90) | −0.011 (−0.26) |
lneip | −0.023 (−1.82) | −0.019 (−1.27) | −0.028 * (−2.27) |
Spatial rho | 0.431 *** (4.46) | −1.494 *** (−6.10) | −0.591 ** (−2.78) |
Variance sigma2 e | 0.031 *** (19.92) | 0.057 *** (19.35) | 0.028 *** (19.85) |
Wx | |||
lngdp | −0.173 (−1.29) | 0.553 * (2.01) | 0.075 (0.20) |
lnge | 0.021 (0.16) | 0.222 (1.18) | 0.651 ** (3.25) |
lnis | 0.074 (0.32) | −1.652 *** (−4.39) | −1.085 * (−2.32) |
lnes | −0.023 (−0.20) | 0.357 (1.66) | 0.221 (1.10) |
lnroad | 0.242 (1.77) | 0.741 *** (3.40) | 0.318 (1.66) |
lner | 0.315 (1.77) | 0.273 (3.40) | 1.230 ** (1.66) |
lnpd | −0.286 (−0.81) | −0.023 (−0.13) | 0.032 (0.08) |
lneip | −0.061 (−0.86) | −0.096 (−0.69) | −0.158 (−1.26) |
R-squared | 0.136 | 0.139 | 0.058 |
Number of OBs | 798 | 798 | 798 |
Variable | LR Direct | LR Indirect | LR Total | |||
---|---|---|---|---|---|---|
Coefficient | T-Statistic | Coefficient | T-Statistic | Coefficient | T-Statistic | |
lnpgdp | 0.229 *** | 4.76 | −0.018 | −0.07 | 0.210 | 0.86 |
lnge | −0.057 * | −2.33 | 0.453 *** | 3.29 | 0.397 ** | 2.89 |
lnis | 0.033 | 0.61 | −0.738 * | −2.29 | −0.706 * | −2.14 |
lnes | −0.074 *** | −3.31 | 0.183 | 1.32 | 0.109 | 0.79 |
lnroad | −0.022 | −1.03 | 0.212 | 1.58 | 0.189 | 1.38 |
lner | −0.120 ** | −2.99 | 0.868 ** | 3.11 | 0.748 ** | 2.68 |
lnpd | −0.011 | −0.25 | 0.014 | 0.05 | 0.003 | 0.01 |
lneip | −0.027 * | −2.26 | −0.094 | −1.12 | −0.121 | −1.46 |
Variable | LR Direct | LR Indirect | LR Total | |||
---|---|---|---|---|---|---|
Coefficient | T-Statistic | Coefficient | T-Statistic | Coefficient | T-Statistic | |
lnpgdp | 0.225 *** | 4.82 | −0.017 | −0.07 | 0.208 | 0.89 |
lnge | −0.057 * | −2.33 | 0.472 *** | 3.47 | 0.415 ** | 3.07 |
lnis | −0.027 | −0.59 | 0.757 ** | 2.88 | 0.730 ** | 2.72 |
lnes | −0.079 *** | −3.54 | 0.207 | 1.49 | 0.128 | 0.93 |
lnroad | −0.023 | −1.04 | 0.231 | 1.73 | 0.208 | 1.53 |
lner | −0.114 ** | −2.89 | 0.975 *** | 3.52 | 0.861 ** | 3.12 |
lnpd | −0.009 | −0.22 | 0.026 | 0.10 | 0.017 | 0.06 |
lneip | −0.027 * | −2.30 | −0.097 | −1.21 | −0.125 | −1.57 |
Variable | LR Direct | LR Indirect | LR Total | |||
---|---|---|---|---|---|---|
Coefficient | T-Statistic | Coefficient | T-Statistic | Coefficient | T-Statistic | |
lnpgdp | 0.227 *** | 4.72 | −0.028 | −0.11 | 0.199 | 0.83 |
lnge | −0.058 * | −2.35 | 0.458 *** | 3.34 | 0.400 ** | 2.93 |
lnis | 0.036 | 0.66 | −0.695 * | −2.20 | −0.659 * | −2.05 |
lnes | −0.074 ** | −3.28 | 0.183 | 1.36 | 0.109 | 0.81 |
lnroad | −0.021 | −0.98 | 0.202 | 1.53 | 0.181 | 1.34 |
lner | −0.117 ** | −2.92 | 0.910 ** | 3.26 | 0.793 ** | 2.84 |
lnpd | −0.012 | −0.27 | 0.001 | 0.0 | −0.011 | −0.04 |
lneip | −0.027 * | −2.32 | −0.096 | −1.15 | −0.123 | −1.49 |
Variable | The Upstream Area | The Midstream Area | The Downstream Area | ||||||
---|---|---|---|---|---|---|---|---|---|
LR Direct | LR Indirect | LR Total | LR Direct | LR Indirect | LR Total | LR Direct | LR Indirect | LR Total | |
lnpgdp | 0.087 (0.89) | 0.072 (0.14) | 0.159 (0.30) | 0.167 * (2.15) | −0.720 (−1.87) | −0.553 (−1.40) | 0.266 * (2.56) | −1.213 ** (−3.14) | −0.947 * (−2.35) |
lnge | −0.094 ** (−2.66) | −0.132 (−0.68) | −0.226 (−1.09) | −0.090 (−1.87) | −0.203 (−1.05) | −0.294 (−1.53) | −0.135 (−1.92) | −0.108 (−0.40) | −0.243 (−0.92) |
lnis | 0.003 (0.03) | −0.035 (−0.05) | −0.032 (−0.04) | −0.002 (−0.03) | −0.918 * (−2.51) | −0.921 * (−2.47) | −0.001 (−0.01) | −0.480 (−1.01) | −0.480 (−0.89) |
lnes | −0.109 ** (−3.04) | −0.170 (−0.85) | −0.279 (−1.36) | −0.081 * (−2.12) | −0.123 (−0.68) | −0.203 (−1.10) | −0.082 (−1.55) | −0.011 (−0.09) | −0.093 (−0.74) |
lnroad | −0.027 (−0.92) | 0.126 (1.12) | 0.099 (0.85) | 0.027 (0.62) | 0.012 (0.06) | 0.039 (0.19) | −0.007 (−0.14) | −0.037 (−0.22) | −0.044 (−0.25) |
lner | −0.201 ** (−2.66) | 0.642 (1.52) | 0.441 (1.00) | −0.097 (−1.66) | −0.045 (−0.14) | −0.141 (−0.44) | −0.273 * (−2.00) | 0.399 (1.05) | 0.127 (0.32) |
lnpd | −0.117 (−1.61) | −0.136 (−0.47) | −0.253 (−0.83) | −0.012 (−0.17) | 0.012 (0.04) | −0.001 (−0.01) | 0.126 (1.19) | −0.159 (−0.36) | −0.033 (−0.07) |
lneip | −0.022 (−1.53) | −0.068 (−1.02) | −0.090 (−1.39) | 0.010 (0.37) | 0.032 (0.23) | 0.043 (0.29) | −0.113 * (−2.52) | 0.217 (1.77) | 0.105 (0.90) |
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Su, H.; Yang, S. Spatio-Temporal Urban Land Green Use Efficiency under Carbon Emission Constraints in the Yellow River Basin, China. Int. J. Environ. Res. Public Health 2022, 19, 12700. https://doi.org/10.3390/ijerph191912700
Su H, Yang S. Spatio-Temporal Urban Land Green Use Efficiency under Carbon Emission Constraints in the Yellow River Basin, China. International Journal of Environmental Research and Public Health. 2022; 19(19):12700. https://doi.org/10.3390/ijerph191912700
Chicago/Turabian StyleSu, Hao, and Shuo Yang. 2022. "Spatio-Temporal Urban Land Green Use Efficiency under Carbon Emission Constraints in the Yellow River Basin, China" International Journal of Environmental Research and Public Health 19, no. 19: 12700. https://doi.org/10.3390/ijerph191912700