Carbon Dioxide Emissions and Their Driving Forces of Land Use Change Based on Economic Contributive Coe ﬃ cient (ECC) and Ecological Support Coe ﬃ cient (ESC) in the Lower Yellow River Region (1995–2018)

: Land use change is the second largest source of greenhouse Intensive on use lower Yellow River Land use Deep study of land used carbon emissions and its inﬂuencing factors in the lower Yellow River area is not only of great signiﬁcance to the environmental improvement in the Yellow River basin, but also can provide references for the research of other basins. Based on this, this paper studies the land use carbon emissions of 20 cities in the lower Yellow River area from 1995 to 2018. The results showed that 1995 2018, land and the increase of the built-up The overall carbon built-up also the analysis of Stochastic by Regression A ﬄ uence, and Technology model. economic contributive coe ﬃ cient (ECC) and ecological support coe ﬃ cient (ESC) of carbon emission in the lower Yellow River area a trend of high in Zhengzhou, Jinan, and Zibo and low in Zhoukou, and Heze, there was no signiﬁcant changes during the study period, which indicates that each city did not achieve the coordinated development of the ecological economy. analysis results of the STIRPAT model that the built-up land the greatest on land the lower Yellow River area. The lower Yellow River area should control the expansion of built-up land, a ﬀ orestation, development of technology, reduction of carbon emissions, and promotion of the high-quality development of the Yellow River Basin.

relationship between the economy and the environment through reducing carbon dioxide emissions. Therefore, comprehensive accounting of land use carbon emissions and an in-depth analysis of the driving mechanisms in China are critical to mitigate GHG emissions, formulate rational carbon emission reduction policies, and ensure coordinated economic development and environmental protection [32,33].
Research on the carbon emissions of land use in China have mainly focused on trends in land use carbon emissions [34], land use carbon effect [35,36], and land use low-carbon optimization [37]. The research on land use carbon emissions has mostly focused on the national [38,39] or provincial level, and provincial-scale research has mostly been concentrated in the provinces of Hunan [40], Jiangsu [41], Hubei [42], Liaoning [43], and Henan [44]. By contrast, few studies have investigated land use carbon emissions within cities and river basins. The Yellow River is the sixth longest river in the world and the second largest river in China. It flows through nine provinces and regions, with a basin area of more than 750,000 km 2 . It is an area rich in natural, historical, and cultural resources in China [45]. The lower Yellow River area flows through Henan and Shandong Provinces, and is an important food production area in China. Thus, the economic and environmental developments are important in the lower Yellow River area. However, with the increase in population and advancing urbanization, carbon dioxide emissions are increasing, and environmental problems are becoming increasingly serious. Therefore, it is necessary to study the carbon emissions of land use in the lower Yellow River area to support high-quality sustainable development [46].
With this background, this study analyzed the spatiotemporal changes in land use carbon emissions in the lower Yellow River area from economic and ecological aspects, and analyzed the influencing factors of land use carbon emissions. To this end, this study used economic and land use data from 20 prefecture-level cities in Henan and Shandong Provinces from 1995 to 2018 and applied the STIRPAT model to them. This study provides a theoretical reference for the low-carbon development of the lower Yellow River area and similar regions, and provides a reference for the optimization of regional land use structure and reducing the environmental problems caused by increasing carbon emissions.

Study Area
The total length of the lower Yellow River area from Taohuayu to Lijin is 786 km, with a drainage area of 23,000 km 2 that accounts for 3% of the area of the Yellow River Basin. This area experiences a temperate monsoon climate. The lower reaches of the Yellow River flow through two provinces, Henan and Shandong. It is an important grain producing area in China, with well-developed agriculture and fertile land. Henan and Shandong are the most populous and agricultural provinces in China, and they are in the stage of urbanization. Based on the close relationship between regional economic development and the lower reaches of the Yellow River and the integrity of prefecture level administrative divisions, we referred to relevant literature [47,48]. This study selected 20 prefecture-level cities in Shandong and Henan provinces, which are the affected areas of the lower reaches of the Yellow River, as the study areas ( Figure 1). Considering the development of the lower Yellow River area in recent decades and the availability of data, this study selected 1995-2018 data to analyze.

Data Sources
The data sources of land use carbon emissions research in the lower Yellow River area mainly included statistical yearbook data, remote sensing imagery data, and data from previous research.

1.
Statistical were obtained from the statistical websites of the investigated cities (Table S1) (Note: This study standardized the data before analysis).

2.
Remote sensing imagery data: Remote sensing imagery data were obtained from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences (http://www.resdc.cn).

3.
Research data: This study obtained carbon emission coefficients of land use (cultivated land, woodland, unused land, built-up land, and water area) [49,50].

Data Sources
The data sources of land use carbon emissions research in the lower Yellow River area mainly included statistical yearbook data, remote sensing imagery data, and data from previous research.

Dynamic Degree of Land Use
The dynamic degree of a single land use type expresses the quantity change of a given land use type within a given time range in a defined research area [51,52] and is calculated as follows: where K is the single dynamic degree of a given land use type and Ua−Ub is the beginning and the end of the considered period of the areas of the land use type.

Dynamic Degree of Land Use
The dynamic degree of a single land use type expresses the quantity change of a given land use type within a given time range in a defined research area [51,52] and is calculated as follows: where K is the single dynamic degree of a given land use type and U a − U b is the beginning and the end of the considered period of the areas of the land use type.

Carbon Emission/Absorption Model of Land Use
Based on land use data from 1995, 2005, 2010, and 2018 in the lower Yellow River area and combined with previous research [53], the carbon emissions of cultivated land, woodland, water area, built-up land, and unused land in 20 prefecture-level cities were calculated in the lower Yellow River area.

1.
The land use carbon emissions of cultivated land, woodland, water area, and unused land, other than built-up land, were calculated as [49]: where EU ce is the land use carbon emissions, S i is the area of the i-th land type, and ε i is the carbon emission coefficient per unit area corresponding to the i-th land use type. Positive values The carbon emissions of built-up land were calculated by multiplying the area of built-up land with carbon sources and sinks, as follows [50]: where EU cb is the carbon emission of built-up land, ξ i is the carbon source coefficient of built-up land, 56.46 t/hm 2 ·a (ton/square hectometer·annually), and δ i is the carbon sink coefficient of built-up land, 2.38 t/hm 2 ·a.

3.
Based on Equations (2) and (3), the total land use carbon emission was obtained, as follows: where EU is the total carbon emission, EU ce is the carbon emission of land use other than built-up land, and EU cb is the carbon emission of built-up land.

Carbon Emission Equity Evaluation Model
According to the Lorenz curve, the Gini coefficient is put forward by the Italian economist Gini, which is widely used as a comprehensive index to investigate the difference of income distribution among residents [54,55]. In this paper, the Gini coefficient and carbon emission characteristics are combined, and the carbon emission equity evaluation model is cited [56] (Figure 2).

Economic Efficiency Model of Carbon Emission
In the carbon emission economic efficiency model, the vertical axis OY represents the percentage of carbon emissions in the whole region, and the horizontal axis OX represents the percentage of GDP in each region. Its significance is to take the GDP of each region as a reference. To emit a certain proportion of carbon, it is necessary to contribute the corresponding proportion of GDP. From the economic point of view, on the basis of assuming the absolute average of carbon emissions, if the proportion of carbon emissions in a certain region is greater than the contribution rate of GDP, it is

Economic Efficiency Model of Carbon Emission
In the carbon emission economic efficiency model, the vertical axis OY represents the percentage of carbon emissions in the whole region, and the horizontal axis OX represents the percentage of GDP Energies 2020, 13, 2600 6 of 18 in each region. Its significance is to take the GDP of each region as a reference. To emit a certain proportion of carbon, it is necessary to contribute the corresponding proportion of GDP. From the economic point of view, on the basis of assuming the absolute average of carbon emissions, if the proportion of carbon emissions in a certain region is greater than the contribution rate of GDP, it is considered that the economic efficiency is relatively low, and it encroaches on the interests of other regions; otherwise, the economic efficiency is relatively high, and it contributes to other regions. Here, the economic contributive coefficient (ECC) is used to measure the fairness of economic contribution of carbon emission in each region [57]. The formula of ECC is as follows: where G j is the GDP of city j, G is the total GDP of the 20 investigated prefecture-level cities in the lower Yellow River area, Cj is the carbon emissions of city j, and C is the sum of the carbon emissions of the 20 prefecture-level cities in the lower Yellow River area. If ECC > 1, the economic contribution rate of city j exceeds the contribution rate of carbon emissions from land use, indicating a relatively high economic efficiency of carbon emissions; if ECC < 1, the economic efficiency of carbon emissions in city j is relatively low.

Ecological Pressure Model of Carbon Emission
In the carbon emission ecological pressure model, the vertical axis OY represents the percentage of carbon emissions in each region in the entire region, and the horizontal axis OX represents the percentage of carbon absorption by carbon sinks in each region. The significance lies in taking carbon absorption of carbon sinks of various regions as a reference. Emitting a certain proportion of carbon, it is necessary to contribute a corresponding proportion of carbon absorption. From the ecological point of view, assuming the absolute average of carbon emissions, if the proportion of carbon emissions in a certain region is greater than the contribution rate of carbon sink to carbon absorption, it infringes the interests of other regions; otherwise, it has relatively high ecological capacity, which has an important contribution to reducing the pressure of carbon emissions on the ecological environment. Here, the ecological support coefficient (ESC) is used to measure the fairness of carbon ecological capacity contribution of each region [57]. The formula of ESC is as follows: where CA j is the amount of carbon absorbed by the carbon sink in city j, and CA is the total amount of carbon absorbed by the carbon sinks of the 20 prefecture-level cities in the lower Yellow River area. If ESC > 1, the contribution rate of carbon sinks in city j to carbon absorption is greater than the contribution rate of carbon emissions, which has a positive effect on the absorption of carbon emissions in the lower Yellow River area and contributes to other regions. If ESC < 1, the contribution rate of the carbon sinks in city j to carbon absorption is less than the contribution rate of carbon emissions and will have a negative impact on the carbon emissions in the lower Yellow River area.

STIRPAT Model
The predecessor of the STIRPAT model is the classic IPAT (Impact, Population, Affluence, Technology) model. The IPAT model was proposed by Ehrlich [58] in 1971, and reflects the relationship between population, economy, technology, and the environment. The formula as follows: where I, P, A, and T represent Impact, Population, Affluence, and Technology, respectively. To make the IPAT model more practical, Diezt [59] proposed the regression model of the IPAT equation, namely the STIRPAT model: where I represents environmental impact; P represents the population; A represents affluence; T represents technology; α is a model coefficient; β, γ, and δ are human-driven indices of P, A, and T; and e is the residual term. To better analyze and describe the relationship between various human factors and the impact of environmental pressure in practical applications, Equation (8) is usually transformed into its logarithmic form as follows: where α is a constant term; β, γ, and δ are the elastic coefficients of the human driving factors P, A, and T, respectively, which means that P, A, and T are the percentage of the change in environmental pressure impact caused by 1% of each change; and e is the residual term.
Based on previous studies [60][61][62], this study extended the STIRPAT model to characterize the impact of environmental pressure by land use carbon emissions, and constructed the following model: where C is the carbon emission of land use (×10,000 t); α is the constant term of the model; P is the population (×10,000 people); A is wealth, characterized by per capita GDP (yuan/person); S is the area of built-up land (km 2 ); I is the contribution of tertiary industry to GDP (%); β, γ, δ, and ρ are the elastic coefficients of P, A, S, and I, respectively; and e is the residual term of the model.

Ordinary Least Squares
Ordinary least squares is a common multivariate linear regression model for parameter estimation. Suppose there are a series of explanatory variables observations X ij and explained variables Y j of i = 1, 2, . . . , m, j = 1, 2, . . . , n [63,64]. The general formula is: where ε is the error term and β 0 is the regression constant. Here, when using the STIRPAT model based on the EViews software (IHS Markit, London, UK), the common least squares method was used to perform regression analysis of the influencing factors of land use carbon emissions.

Land Use Changes
To analyze the changes in land use area in the lower Yellow River area from 1995 to 2018, the land use change area and dynamic degree of single land use types in the lower Yellow River area were calculated using Equation (1) ( Table 2). The areas of cultivated land and unused land decreased continuously, while the areas of built-up land and water increased substantially (Table 2)

Spatiotemporal Trends in Land Use Carbon Emissions
Intense changes in land use were observed in the lower Yellow River area during 1995-2018. This study calculated the carbon emissions of all cities in the lower Yellow River area using Equations (2) and (3). Overall, carbon emissions in the lower Yellow River area increased from 1995 (1.6 × 10 8 t) to 2018 (1.97 × 10 8 t). This rise was attributed to increases in carbon sources, where the main land use type acting as a carbon source was built-up land followed by cultivated land. In 2010, the carbon emissions of built-up land accounted for more than 73% of the total carbon sources and this proportion increased over time.
To further analyze the contribution of built-up land to carbon sources, the proportions of carbon sources of built-up land was calculated for the 20 prefecture-level cities in 2010 and 2018 (Table 3). In 2010, the proportions of built-up land of all carbon sources of 15 cities exceeded 70%. By 2018, the proportion increased and exceeded 80% in Zhengzhou, Jinan, Zibo, and Laiwu. By contrast, woodland accounted for the largest proportion of total carbon sinks, while water areas and unused land accounted for a small proportion; the magnitudes of change in these proportions during the study period were not significant. To facilitate the comparison of regional carbon emission differences, this study divided carbon emissions into five levels according to the land use carbon emissions in 1995-2018 using the natural Energies 2020, 13, 2600 9 of 18 interruption method: 1-3.5 × 10 6 t, very low carbon emissions; 3.5-6.5 × 10 6 t, low carbon emissions; 6.5-8 × 10 6 t, moderate carbon emissions; 8-11.5 × 10 6 t, high carbon emissions; and 11.5-18 × 10 6 t, very high carbon emissions indicative of serious pollution. Using Equation (4), the distribution of carbon emissions of prefecture-level cities was calculated in the lower Yellow River area from 1995 to 2018 (Figure 3).
Energies 2020, 13, x FOR PEER REVIEW 10 of 19 carbon emissions increased along with continuous economic development. Moreover, Zhengzhou, Jinan, Zibo, and Jining did not adequately address environmental issues while developing their economies. By contrast, Dongying also had relatively high GDP growth, but its carbon emission increment was not high, indicating that Dongying better balanced environmental protection efforts and economic development.  To analyze the relationship between the growth of carbon emissions and GDP of the 20 cities in the lower Yellow River area, the GDP growth and carbon emissions were calculated for the 20 cities at the prefecture level from 1995 to 2018 (Table 4). Zhengzhou, Jinan, Zibo, and Jining had higher GDP growth, as well as relatively high increments of carbon emissions (Table 4), indicating that carbon emissions increased along with continuous economic development. Moreover, Zhengzhou, Jinan, Zibo, and Jining did not adequately address environmental issues while developing their economies. By contrast, Dongying also had relatively high GDP growth, but its carbon emission increment was not high, indicating that Dongying better balanced environmental protection efforts and economic development.

ECC of Carbon Emissions
In order to analyze the economic benefits of carbon emission of 20 cities in the lower Yellow River area, the ECC value of carbon emissions of each city was obtained by Equation (5) 18.624 billion yuan) grew rapidly. Thus, these two cities did not effectively balance economic development and emission reductions during these 10 years of development.
According to Table 3 and Figure 4, the GDP of Zhengzhou, Jinan, and Zibo was relatively high, but their ECC was always at a high level. It shows that although these three cities produced large amounts of carbon dioxide emissions during the course of economic development, the economic efficiency of their carbon emissions were higher than those of other cities in the lower Yellow River area.  According to Table 3 and Figure 4, the GDP of Zhengzhou, Jinan, and Zibo was relatively high, but their ECC was always at a high level. It shows that although these three cities produced large amounts of carbon dioxide emissions during the course of economic development, the economic efficiency of their carbon emissions were higher than those of other cities in the lower Yellow River area.

ESC of Carbon Emissions
To further analyze the ecological development of the 20 prefecture-level cities in the lower Yellow River area, this study calculated the ESCs of each city using Equation (6), and divided the values into five levels ( Figure 5): 0-1, very low; 1-1.5, low; 1.5-2, moderate; 2-3, high; and 3-4, very high. During 1995-2018, the ESCs of Jinan, Zibo, Laiwu, and Jiaozuo all exceeded 1.5, indicating that these cities achieved a better balance of environmental production and economic development. Among them, the ESCs of Laiwu always exceeded 2 and even surpassed 3 in 1995 and 2010, indicating that the contribution rate of carbon sequestration to carbon absorption in Laiwu was greater than that

ESC of Carbon Emissions
To further analyze the ecological development of the 20 prefecture-level cities in the lower Yellow River area, this study calculated the ESCs of each city using Equation (6), and divided the values into five levels ( Figure 5): 0-1, very low; 1-1.5, low; 1.5-2, moderate; 2-3, high; and 3-4, very high. During 1995-2018, the ESCs of Jinan, Zibo, Laiwu, and Jiaozuo all exceeded 1.5, indicating that these cities achieved a better balance of environmental production and economic development. Among them, the ESCs of Laiwu always exceeded 2 and even surpassed 3 in 1995 and 2010, indicating that the contribution rate of carbon sequestration to carbon absorption in Laiwu was greater than that of carbon sources to carbon emissions; overall, this had a beneficial effect on the elimination of carbon emissions in the lower Yellow River area.
of carbon sources to carbon emissions; overall, this had a beneficial effect on the elimination of carbon emissions in the lower Yellow River area. During the study period, 11 cities (Dongying, Binzhou, Dezhou, Liaocheng, Puyang, Jining, Heze, Shangqiu, Kaifeng, Zhoukou, and Xuchang) always had very low ESCs, indicating that the contribution rate of carbon sinks to carbon emission absorption in these 11 cities was less than that of carbon sources. Thus, these 11 cities did not effectively promote regional environmental protection efforts during 1995-2018. Notably, Zhengzhou had moderate ESCs in 1995 and 2010, but a low ESC in 2018. As the provincial capital, the economic development of Zhengzhou is very important. From 2010 to 2018, its built-up land increased by 987 km 2 , which led to the continuous increase of carbon emissions of built-up land and decrease in the ecological carrying capacity coefficient. During the study period, 11 cities (Dongying, Binzhou, Dezhou, Liaocheng, Puyang, Jining, Heze, Shangqiu, Kaifeng, Zhoukou, and Xuchang) always had very low ESCs, indicating that the contribution rate of carbon sinks to carbon emission absorption in these 11 cities was less than that of carbon sources. Thus, these 11 cities did not effectively promote regional environmental protection efforts during 1995-2018. Notably, Zhengzhou had moderate ESCs in 1995 and 2010, but a low ESC in 2018. As the provincial capital, the economic development of Zhengzhou is very important. From 2010 to 2018, its built-up land increased by 987 km 2 , which led to the continuous increase of carbon emissions of built-up land and decrease in the ecological carrying capacity coefficient.

STIRPAT Model Results
According to Equation (10), the data of land use carbon emissions, population, per capita GDP, built-up land area, and tertiary industry's proportion of GDP of the 20 prefecture-level cities in the lower Yellow River area were standardized. Ordinary least square regression analysis was carried out using the EViews software (IHS Markit, http://www.eviews.com/home.html), and the model regression results were obtained (Table 5). The degree of fit of the model reached 99.43%, and the F-statistic passed the significance level of 1% [6], indicating that the model fit very well (Table 5). At the 1% level, land use carbon emissions were significantly negatively correlated with per capita GDP and tertiary industry's proportion of GDP, but significantly positively correlated with built-up land area; there was no significant correlation between land use carbon emissions and population. The coefficients of per capita GDP, tertiary industry's proportion of GDP, and built-up land area were −0.0338, −0.0511, and 1.024, indicating that for every 1% increase in per capita GDP or tertiary industry's proportion of GDP, the carbon emissions from land use decreased by 0.0338% or 0.0511%, respectively, and for every 1% increase in built-up land area, the carbon emissions from land use increased by 1.024%.
The reason for the negative correlation between land use carbon emissions and per capita GDP may be that as people's economic situation improves so does their understanding of environmental problems, fostering an interest in the development of a low-carbon cycle. Similarly, the negative correlation between the tertiary industry's proportion of GDP and land use carbon emissions may be explained by technological advancements that occur with continuous socioeconomic development, resulting in strengthened control of carbon emissions due to technological improvements and cleaner production processes. Finally, the positive correlation between built-up land area and land use carbon emissions showed that built-up land was the main source of carbon emissions, and that the expansion of built-up land has a clear incremental effect on carbon emissions.

Discussion
This study combined multiple models to analyze the spatiotemporal characteristics and driving factors of land use carbon emissions from the lower Yellow River area from different perspectives. It provides a basis for improving land use in the later stage of economic development and achieving a balance between environmental protection efforts and high-quality socioeconomic development in the Yellow River Basin. Moreover, it enriches the relevant literature on land use carbon emissions and provides a reference for regional environmental and ecological protection.
This study combined analyses of ESC, ECC, and driving factors of carbon emissions. The results showed that land use carbon emissions in the lower Yellow River area are generally high in the north and south, and low in the east and west. Moreover, with rapid economic development in China, the carbon emissions of the prefecture-level cities increased to varying degrees. In areas with rapid economic development, the corresponding land use carbon emissions also grew rapidly, consistent with previous work [29]. The economy of Dongying grew rapidly in 1995 and 2018, but its carbon emissions did not increase much. Other cities should learn from the governance of Dongying City to ensure the coordinated development of economy and ecology. In the lower Yellow River area, ECC and ESC values of carbon emissions were higher in the east and west, and lower in the north and south. During the study period, despite economic development, the overall ECC of carbon emissions in the lower Yellow River area did not increase. This showed that some cities did not adequately support environmental protection efforts, resulting in an overall low ECC of carbon emissions. Furthermore, there were no obvious changes in the ESC over time. This showed that the environmental problems in the lower Yellow River area have not improved, and greater effort should be made to balance economic development and environmental protection.
From the influencing factors of land use carbon emissions in the lower Yellow River area, built-up land area had the greatest impact on carbon emissions, followed by tertiary industry, whereas per capita GDP had the lower impact. Moreover, land use carbon emission was negatively correlated with tertiary industry and positively correlated with built-up land area, consistent with a previous study [6]. Unexpectedly, land use carbon emission was also negatively correlated with per capita GDP. This might be because, as people's economic situation improves, they may become more aware of environmental problems and strive to reduce carbon emissions from land use.

Conclusions
Land use change is the main driver of carbon emissions, which can damage the ecological environment. Intensive study on land use carbon emissions is of great significance to alleviate environmental pressure, formulate carbon emission reduction policies, and protect ecological development. The lower Yellow River has experienced rapid industrialization and urbanization with drastic land use changes during 1995-2018. This paper takes the lower Yellow River as the research area, through calculating the carbon emissions of land use in 20 cities in the lower Yellow River, combined with the economy and ecology of each city, and analyzes the driving factors of land use carbon emissions. Research found, from 1995 to 2018, land use change was characterized by the decrease of the ecological land and the increase of the built-up land. Overall, carbon emissions increased; built-up land was the main carbon source, while woodland was the main carbon sink. The economic contribution coefficient of carbon emission in the lower Yellow River has not changed much as a whole. The ECC value of Zhengzhou, Jinan, and Zibo has always been at a high level, which shows that these three cities pay attention to energy conservation and emission reduction while developing GDP. The ESC value of 20 cities in the lower Yellow River was generally on the low side. Considering the driving factors of carbon emission, the continuous increase of built-up land in the lower reaches of the Yellow River leads to the same increase of carbon sink and continuous decrease of carbon source. Land use carbon emission was significantly negatively correlated with per capita GDP and tertiary industry's proportion of GDP, and significantly positively correlated with built-up land area.
In this paper, the ECC, ESC, and driving factors of land use carbon emission in the lower Yellow River were analyzed to enrich the research of carbon emission in the basin. Most scholars analyzed the driving factors of carbon emission from the economic aspect. This paper adds ecological factors, which provide more reference for other scholars. At the same time, it laid the theoretical foundation for the coordinated development of economy and ecology in the lower Yellow River. While developing the economy, each region should pay more attention to ecological development and control the rapid expansion of built-up land, while promoting the economical and intensive use of existing built-up land.
In addition to the above conclusions, this study had some limitations. The land use carbon emission was related to many factors, such as the first industry, the second industry, the environmental awareness of people, and so on. This study combined economic and ecological data. To analyze, in the future, more factors should be considered to study the land use carbon emission.