*3.2. Evolution of Net Carbon Emissions in the Beijing-Tianjin-Hebei Region*

The average annual net carbon emission during 1990–2020 was 313.93 million tons, and the net carbon emissions of Hebei Province changed most significantly, increasing by 317.71 million tons during the study period, followed by Tianjin and Beijing. From the perspective of regions within the study area, the changing trend of net carbon emissions of Hebei Province is similar to that of Beijing and Tianjin. Besides, the evolution of carbon emissions in the Beijing-Tianjin-Hebei region over these 30 years was divided into three stages, according to the change of net carbon emissions, the industrial development status, and energy consumption status in the study area, as shown in Figure 3.

**Figure 3.** Changing trends of net carbon emissions in the Beijing-Tianjin-Hebei region from 1990– 2020 (million tons). **Figure 3.** Changing trends of net carbon emissions in the Beijing-Tianjin-Hebei region from 1990–2020 (million tons).

Phase I (1990–2000) is the stage of slow increase in net carbon emissions, which increased from 112.86 million tons to 178.54 million tons but was still significantly lower than the average level of the whole study period. This is mainly due to the fact that the study area was still in the early industrialization stage during this phase, with a low urbanization level and low consumption of various energy sources, which led to relatively lower net carbon emissions. Phase I (1990–2000) is the stage of slow increase in net carbon emissions, which increased from 112.86 million tons to 178.54 million tons but was still significantly lower than the average level of the whole study period. This is mainly due to the fact that the study area was still in the early industrialization stage during this phase, with a low urbanization level and low consumption of various energy sources, which led to relatively lower net carbon emissions.

Phase II (2000–2010) is the stage of rapid growth of net carbon emissions, which rapidly increased from 178.54 million tons to 436.83 million tons, with the average annual growth rate reaching 144.66%. This is mainly due to the national focus on the development of heavy industries and the relatively loose macro production capacity policies during this period, which led to the lower entry requirement of high energy-consuming, high-emission, and low-efficiency enterprises into the study area, thus resulting in the rapid increase Phase II (2000–2010) is the stage of rapid growth of net carbon emissions, which rapidly increased from 178.54 million tons to 436.83 million tons, with the average annual growth rate reaching 144.66%. This is mainly due to the national focus on the development of heavy industries and the relatively loose macro production capacity policies during this period, which led to the lower entry requirement of high energy-consuming, high-emission, and low-efficiency enterprises into the study area, thus resulting in the rapid increase of the net carbon emissions.

of the net carbon emissions. The third phase (2010–2020) is the stage of steady increase of net carbon emissions, with the total carbon emissions increasing steadily from 436.83 million tons to 525.31 million tons. The average carbon emission level during this phase was 1.58 times that of the second phase, but the average annual growth rate was only 20.25%, which is much lower than that of the second phase. This is mainly due to the improvement of energy efficiency under the influence of the national policies on energy saving and emission reduction and application of advanced technologies. In particular, the implementation of the policies of "peak carbon dioxide emissions" and "carbon neutrality" imposed important limitations The third phase (2010–2020) is the stage of steady increase of net carbon emissions, with the total carbon emissions increasing steadily from 436.83 million tons to 525.31 million tons. The average carbon emission level during this phase was 1.58 times that of the second phase, but the average annual growth rate was only 20.25%, which is much lower than that of the second phase. This is mainly due to the improvement of energy efficiency under the influence of the national policies on energy saving and emission reduction and application of advanced technologies. In particular, the implementation of the policies of "peak carbon dioxide emissions" and "carbon neutrality" imposed important limitations on the increase of carbon emissions.

#### on the increase of carbon emissions. *3.3. Variation of Carbon Emissions from Land Use Change*

*3.3. Variation of Carbon Emissions from Land Use Change*  The carbon emissions from land use change are shown in Table 3, which were estimated on the basis of the land use data and energy consumption data of the Beijing-Tianjin-Hebei region during 1990–2020. The carbon emissions from land use change in the Beijing-Tianjin-Hebei region showed an overall increasing trend during 1990–2020, with the total carbon emissions increasing from 112.86 million tons in 1990 to 525.30 million tons The carbon emissions from land use change are shown in Table 3, which were estimated on the basis of the land use data and energy consumption data of the Beijing-Tianjin-Hebei region during 1990–2020. The carbon emissions from land use change in the Beijing-Tianjin-Hebei region showed an overall increasing trend during 1990–2020, with the total carbon emissions increasing from 112.86 million tons in 1990 to 525.30 million tons in 2020, with an overall growth rate of 365.46% during these 30 years (Table 3). Among the major sources of carbon emissions, the carbon emissions from cropland decreased slowly from 4.66 million

in 2020, with an overall growth rate of 365.46% during these 30 years (Table 3). Among the major sources of carbon emissions, the carbon emissions from cropland decreased

tons in 1990 to 4.15 million tons in 2020, with a total decrease of 0.52 million tons and an average annual decrease of 17.12 thousand tons in these 30 years, which is mainly due to the slowly decreasing trend of cropland area in the study area during the study period. However, the carbon emissions from built-up area as another major source of carbon emissions increased rapidly from 111.51 million tons in 1990 to 524.51 million tons in 2020, with an overall growth rate of 370.37% during the study period. In particular, carbon emissions from built-up area accounted for 95.99–99.22% of the total carbon emissions, so built-up area served as the main carbon emission source in the Beijing-Tianjin-Hebei region during the study period. The carbon sinks included the forest land, grassland, water area, and barren land, the effects of which on the carbon absorption shoed a descending order. Specifically, the carbon absorption effect of the forest land was the most significant, accounting for 83.58–89.56% of the total carbon absorption amount of the study area, while the grassland and water body accounted for 9.62–15.49% and 0.47–0.59% of the total carbon absorption amount of the study area, respectively. By contrast, the carbon sequestration effect of barren land was the least significant, accounting for only 0.24–0.35% of the total carbon absorption amount of the study area. It is particularly notable that the ratio of the carbon emissions from the built-up area to the carbon absorption from the forest land ranged between 40.26 and 185 during 1990–2020. In other words, the carbon emissions from the built-up area were so large that the carbon absorption effect of the forest land failed to offset the carbon source effect of built-up area, which is the underlying reason for the continuous increase of the total carbon emissions in the study area during the study period.

**Table 3.** Carbon emissions from land use change in the Beijing-Tianjin-Hebei region during 1990–2020 (million tons).


### *3.4. Effects of Influencing Factors of Carbon Emissions from Land Use Change*

The decomposition of the variation of carbon emissions in the Beijing-Tianjin-Hebei region during 1990–2020 with the LMDI model revealed the contribution value and contribution rate of five influencing factors of carbon emissions, i.e., carbon emission intensity per unit of land, land use structure, land use intensity per unit of GDP, GDP per capita, and population size in Figure 4. The results showed that there was obvious annual variation trends of these influencing factors of carbon emissions from land use change in the study area (Figure 4). Besides, Figure 5 shows the cumulative contribution rate of these influencing factors. There were remarkable differences in the contribution value between various influencing factors of the carbon emissions from land use change (Figure 5). The absolute contribution values of each influencing factor to the variation of carbon emissions from land use change during 1990–2020 in a descending order were land use intensity per unit of GDP > GDP per capita > land use structure > carbon emission intensity per unit of land > population size. The land use intensity per unit of GDP was the biggest restraining factor of the increase of carbon emissions, and the remaining four factors all had positive effects on the increase of carbon emissions, among which GDP per capita had the greatest promotion effects on the increase of carbon emissions (Figure 5).

**Figure 4.** Contribution value of the influencing factors to variation of carbon emissions from land use change in the Beijing-Tianjin-Hebei region. **Figure 4.** Contribution value of the influencing factors to variation of carbon emissions from land use change in the Beijing-Tianjin-Hebei region. **Figure 4.** Contribution value of the influencing factors to variation of carbon emissions from land use change in the Beijing-Tianjin-Hebei region.

**Figure 5.** Cumulative contribution rate of influencing factors to the variation of carbon emissions from land use change in the Beijing-Tianjin-Hebei region. **Figure 5.** Cumulative contribution rate of influencing factors to the variation of carbon emissions from land use change in the Beijing-Tianjin-Hebei region. **Figure 5.** Cumulative contribution rate of influencing factors to the variation of carbon emissions from land use change in the Beijing-Tianjin-Hebei region.

This study suggested that the land use factors played an important role in influencing the carbon emissions. For example, there were extremely unstable effects of the carbon emission intensity per unit of land on the carbon emissions, which showed a promoting effect during 1990–2015 and a restraining effect during 2015–2020, indicating uncertainties of the role of carbon emission intensity per unit of land in influencing the carbon emissions from land use change. Nevertheless, the cumulative contribution rate of the carbon emission intensity per unit of land was 89.06% during the study period. Besides, the land use structure had an overall positive effect on the carbon emissions from land use change This study suggested that the land use factors played an important role in influencing the carbon emissions. For example, there were extremely unstable effects of the carbon emission intensity per unit of land on the carbon emissions, which showed a promoting effect during 1990–2015 and a restraining effect during 2015–2020, indicating uncertainties of the role of carbon emission intensity per unit of land in influencing the carbon emissions from land use change. Nevertheless, the cumulative contribution rate of the carbon emission intensity per unit of land was 89.06% during the study period. Besides, the land use structure had an overall positive effect on the carbon emissions from land use change This study suggested that the land use factors played an important role in influencing the carbon emissions. For example, there were extremely unstable effects of the carbon emission intensity per unit of land on the carbon emissions, which showed a promoting effect during 1990–2015 and a restraining effect during 2015–2020, indicating uncertainties of the role of carbon emission intensity per unit of land in influencing the carbon emissions from land use change. Nevertheless, the cumulative contribution rate of the carbon emission intensity per unit of land was 89.06% during the study period. Besides, the land use structure had an overall positive effect on the carbon emissions from land use change

during the study period, with a cumulative contribution rate of 64.3%. It is notable that the contribution value of the land use structure showed a significant increase during 2015–

during the study period, with a cumulative contribution rate of 64.3%. It is notable that

during the study period, with a cumulative contribution rate of 64.3%. It is notable that the contribution value of the land use structure showed a significant increase during 2015–2020, which is mainly because the built-up area expanded rapidly during this period, which led to the remarkable increase of carbon emissions from land use change. It is therefore of great significance to the control of carbon emissions to carry out the reasonable layout of land use structure. By contrast, the land use intensity per unit of GDP was the primary restraining factor of the carbon emissions from land use change in the study area, which had a suppressive effect on the net carbon emissions throughout the study period, with a cumulative contribution reaching −385.47%. This indicates that it is feasible to achieve a sustainable development status of the land use by promoting the economic development and adopting some reasonable means.

There was always a positive contribution value of GDP per capita to the increase of carbon emissions, i.e., GDP per capita had played a positive role in promoting the increase of carbon emissions from land use change during the study period. In particular, the cumulative contribution of GDP per capita was the highest among these influencing factors, indicating that GDP per capita played the most important role in promoting the increase of carbon emissions from land use change. In fact, GDP per capita represents the regional economic development level as well as the affluence level, and the rapid economic development not only brings abundant material achievements but also generates a large amount of carbon emissions; it is therefore necessary to pay more attention to the factors of economic development in future research and the practice of carbon emission reduction. Additionally, the population size always had a promoting effect on the increase of carbon emissions during 1990–2015, which is similar to GDP per capita. The cumulative contribution value and cumulative contribution rate of population size reached 1.05 million tons and 30.61%, respectively, indicating that the population size is also one of the most important factors promoting the increase of carbon emissions from land use change. This is primarily because the population growth leads to the increase in energy consumption and further results in more carbon emissions from energy consumption on the land.

#### **4. Discussion**

There is an overall high reliability of the results of this study, which were generally consistent with previous studies. For example, some previous studies suggested that the growth of GDP per capita resulting from expansion of coal intensive industries was a major factor driving carbon emissions in China [44,46], and this study also suggested that GDP per capita played a dominant role in promoting the increase of carbon emissions in the Beijing-Tianjin-Hebei region, indicating there was a consistent major driving factor of the Beijing-Tianjin-Hebei region and the whole of China. On the one hand, the Beijing-Tianjin-Hebei region and other parts of China both used to heavily depend on coal, with a slight difference in the technological level of energy utilization among these regions, which led to a similar pattern of carbon emissions of the Beijing-Tianjin-Hebei region and other parts of China. On the other hand, the Beijing-Tianjin-Hebei region, as one of the three major urban agglomerations in China, generally kept pace with the rapid economic development of the whole of China, leading to a similar change of GDP per capita and subsequently carbon emissions. More importantly, this study estimated the carbon emissions with the data extracted from the authoritative statistical yearbooks and carried out a decomposition analysis of influencing factors of carbon emissions with the relatively mature LMDI model, which were both generally consistent with previous studies and therefore guaranteed the reliability of the results of this study.

There are still some uncertainties in the results of this study due to the limitation of data accuracy, and it is especially necessary to further improve the estimation of the carbon emission (or absorption) coefficients based on dynamic observation data in the future. For example, the carbon emission coefficient of the State Grid Corporation of China has been adjusted from 0.6101 tCO2/MWh to 0.5810 tCO2/MWh in 2022, according to the latest "Corporate Greenhouse Gas Emissions Accounting Methodology and Reporting Guidelines for Power Generation Facilities (2022 Revised Edition)". This carbon emission coefficient has declined slightly by only 4.77% after years of technical progress; it is therefore still feasible to assume that the carbon emission coefficient of electricity kept constant in past decades. Nevertheless, it is still necessary to use some dynamic carbon emission coefficients, according to the specific conditions, to more accurately reveal the effects of technical progress and other factors on carbon emissions in the future.

Although there are still some uncertainties, this study still successfully revealed the effects of various influencing factors of carbon emissions from land use change. This study has accordingly proposed the following policy recommendations, which can contribute to promoting the improvement of the lower carbon emission of land use and support the synergetic development of the Beijing-Tianjin-Hebei region.

(1) Optimization of the land use structure and the spatial layout of land use

It is necessary to carry out land use in a more economical and intensive way, making full use of the barren land and a large amount of idle land by giving priority to turning the barren land and idle land into cropland, forest land, and grassland. It is also necessary to carry out a moderate return of cropland to forest land and grass land. It is particularly urgent to control the proportion of built-up area and increase the area of carbon sinks by planting trees and optimizing the spatial layout of public green space, which can contribute to achieving environmental improvement and low-carbon development in the Beijing-Tianjin-Hebei region.

(2) Adjustment of the energy structure and development of new cleaner energy sources

The current energy consumption of the Beijing-Tianjin-Hebei region dominantly depends on fossil energy, especially coal, while the proportion of cleaner energy is still very low, and it is therefore very necessary to adjust the energy structure and reduce the dependence on fossil energy. To achieve this end, it is necessary to promote the development of new cleaner energy sources and to encourage the development of advanced energy-saving technologies, e.g., the Carbon Capture and Storage technologies, which can effectively reduce carbon emissions.

(3) Optimization of the industrial structure and promotion of the development of the tertiary industry

The increase of GDP of the Beijing-Tianjin-Hebei region still heavily depends on the secondary industry, which plays an important role in the increasing the carbon emissions. By contrast, the primary and tertiary industries have relatively small carbon emissions. It is therefore necessary to carry out optimization of the industrial structure by "retreating from secondary industry and developing the tertiary industries", which can make a considerable contribution to the energy saving and carbon emission reduction in the Beijing-Tianjin-Hebei region.

#### **5. Conclusions**

This study estimated the carbon emissions in the Beijing-Tianjin-Hebei region during 1990–2020 based on the carbon emission coefficients, and it revealed the quantitative relationship between land use change and carbon emissions with the decomposition analysis of the main influencing factors of carbon emissions from land use change. The major conclusions are as follows: (1) the cropland, forest land, grassland, and barren land in the study area all showed a decreasing trend during 1990–2020, among which the cropland decreased most significantly and the built-up area increased significantly due to the accelerated urbanization. (2) The total carbon emissions in the study area increased from 112.86 million tons in 1990 to 525.31 million tons in 2020, with a growth rate of 365.46%. Built-up area was the main carbon source, the carbon emissions of which increased rapidly from 111.51 million tons in 1990 to 524.52 million tons in 2020, with a growth rate of 370.37%. The forest land accounted for 83.58–89.56% of the total carbon absorption, but it still failed to offset the carbon emissions of the built-up area. (3) GDP per capita contributed most to the increase

of the carbon emissions, resulting in a cumulative increase of carbon emissions by 94.78 million tons. While the land use structure, carbon emission intensity per unit of land, and population size led to the increase of carbon emissions by 18.11 million tons, 22.95 million tons, and 8.67 million tons, respectively. By contrast, the land use intensity per unit of GDP had a restraining effect on the carbon emissions, making the carbon emissions decrease by 103.26 million tons in total. This study accurately revealed the variation of net carbon emissions from land use change and the effects of influencing factors of carbon emissions from land use change in the Beijing-Tianjin-Hebei region, and all the conclusions of this study can provide a firm scientific basis for improving the regional land use planning and for promoting the low-carbon economic development of the Beijing-Tianjin-Hebei region.

**Author Contributions:** H.Y. (Haiming Yan) and S.Z.: investigation, data curation, writing—original draft preparation, writing—review and editing, and funding acquisition; H.Y. (Huicai Yang): conceptualization, methodology, supervision, and project administration; H.Y. (Haiming Yan) and X.G.: software, validation, and visualization. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was funded by the Major Project of Humanities and Social Science Research of Hebei Education Department (ZD201907), the Science and Technology Project of Hebei Education Department (BJ2019045), and the National Natural Science Foundation of China (51909052).

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We are very grateful for the helpful inputs from the editor and anonymous reviewers.

**Conflicts of Interest:** The authors declare no conflict of interest.
