4.1. Analysis of the Expansion of Built-Up Areas
4.1.1. Evaluation Index of the Expansion of Built-Up Areas
The extraction results and related parameters of the built-up areas are shown in
Table 2 and
Figure 4 and
Figure 5.
To explore the speed and intensity of urban expansion and evaluate its coordination [
31,
32,
33,
34], four factors were used: expansion rate, expansion intensity, compaction degree, and fractal dimension. The results of these factors are shown in
Figure 6.
Results:
It can be seen from
Figure 6 that from 1992 to 1995, the built-up area grew slowly, with an expansion rate of 2.74 and an expansion intensity of 0.06. From 1995 to 2000, Nanjing built-up areas entered a stage of rapid growth, with an expansion rate of 313% and an expansion intensity of 342%. From 2000 to 2005, the constructed area entered a stage of rapid growth. The expansion rate increased by 113% compared with the previous stage, and the expansion intensity increased by 235%. From 2005 to 2010, the growth rate of built-up areas slowed down; the expansion rate was 86% lower than that of the previous stage, and the expansion intensity was 68% lower. From 2010 to 2013, Nanjing built-up areas entered a rapid growth stage again, with an expansion rate of 308% and an expansion intensity of 379% compared with the previous stage. From 2013 to 2015, the growth rate of built-up areas slowed down again, with the expansion rate reduced by 70% and the expansion intensity reduced by 57% compared with the previous stage. From 2015 to 2020, the growth rate of built-up areas was stable, with an expansion rate of 2.90 and expansion intensity of 0.40, similar to the initial stage of urban expansion.
The density of built-up areas in Nanjing gradually decreased from 0.42 to 0.23, and the differentiation dimension of built-up areas slightly changed from 1.31 to 1.39.
Discussion:
From 1992 to 2020, a high growth rate for built-up areas was maintained in Nanjing, and expansion speed and intensity were at a high level; expansion changed from irregular and disorderly to comprehensive and balanced development, reaching a coordinated and orderly state. This coincided with Nanjing’s policy of promoting urban–rural integration, improving urban construction and strengthening regional ties.
4.1.2. Analysis of Spatiotemporal Changes in Built-Up Areas
The center of gravity model was applied to investigate the spatiotemporal changes of built-up areas in Nanjing [
31,
32,
33,
34]. The results are shown in
Table 3, and the corresponding distributions are shown in
Figure 7.
Results:
The whole city developed eastward from 1992 to 1995, developed southward from 1995 to 2000, developed southward from 2000 to 2005, developed southwest from 2005 to 2010, developed significantly southwest from 2010 to 2013, developed southward from 2013 to 2015, and developed less westward from 2015 to 2020.
As observed in
Table 4, it can be seen that the center of gravity of built-up areas moved 412.11 m to the east from 1992 to 1995, moved 1942.11 m to the south from 1995 to 2000, moved 550.58 m to the south from 2000 to 2005, moved 74.43 m to the southwest from 2005 to 2010, moved 4143.07 m to the south from 2010 to 2013, moved 242.31 m to the south from 2013 to 2015, and moved 230.74 m to the west from 2015 to 2020.
The standard deviation ellipse area of built-up areas increased by 32 km2 with an annual growth rate of 9.2% from 1992 to 1995, increased by 15 km2 with an annual growth rate of 2.0% from 1995 to 2000, increased by 60 km2 with an annual growth rate of 7.36% from 2000 to 2005, increased by 17 km2 with an annual growth rate of 1.52% from 2005 to 2010, increased by 643 km2 with an annual growth rate of 89.31% from 2010 to 2013, decreased by 10 km2 with an annual growth rate of −0.57% from 2013 to 2015, and increased by 15 km2 with an annual growth rate of 0.34% from 2015 to 2020.
From 1992 to 2020, the overall gravity center of Nanjing built-up areas moved from the northwest to the southeast, and the standard deviation ellipse of built-up areas also showed an increasing trend from northwest to southeast.
Discussion:
From 1995 to 2010, Nanjing put forward an urban construction plan for cross-river development, and the Pukou area developed rapidly as a result. From 2010 to 2020, Nanjing proposed a construction plan for the Southern New Area, and the Jiangning, Lishui, and Gaochun regions have developed rapidly as a consequence. In general, the direction of Nanjing’s urban gravity center and standard deviation ellipse was the same as that of urban planning policy; the urban development focus of Nanjing was developing from north to south, and the development area was gradually shifting from Pukou to Jiangning, Lishui, and Gaochun.
Overall, the direction of Nanjing’s urban gravity center and standard deviation ellipse was consistent with the direction of urban planning policies. From 1995 to 2010, Nanjing proposed the urban construction plan for cross-river development, and the Pukou area developed rapidly. During the same period, the standard deviation ellipse of Nanjing expanded violently towards the north. From 2010 to 2020, Nanjing proposed a construction plan for the Southern New Area, with rapid development in the Jiangning, Lishui, and Gaochun regions. During the same period, Nanjing’s urban gravity center shifted significantly southward, and the standard deviation ellipse expanded violently towards the south.
4.2. Analysis of the Expansion of Built-Up Areas vs. Environmental Parameters
Nighttime lightsnighttime lights
The development and expansion of built-up areas is a systematic process affected by many uncertain factors. Scholars [
35] point out that the dynamic factors of urban spatial expansion include natural conditions [
36], urban economic development [
37], urban population growth [
38], urban transportation network development [
39], government policy intervention, etc., and the expansion of built-up areas in Nanjing is also driven and restricted by these factors. Along with our country in the 21st century, the development of science and technology and improvements to national planning regulations [
40] have led to the natural conditions of urban construction direction gradually becoming smaller while the influence of urban economy, urban population, and government planning and policy factors that affect the direction of urban construction has increased.
The Spearman correlation coefficient can effectively measure the relationship between two variables and has been widely used in economics, ecology, and social science research, such as in the studies of Wang [
41] and Zheng [
42]. In order to unveil the correlations between nighttime lights data and the urban parameters in Nanjing, this article used nighttime lighting data as the independent variable and other urban parameters as the dependent variable and analyzed them separately. The formula for calculating the Spearman correlation coefficient is shown in
Section 3.3, and the results are shown in
Table 4.
According to the above analysis of urban social parameters, the correlation coefficient between the urban population, urban non-agricultural GDP, urban electricity consumption, drainage facilities, and nighttime lights was close to 1, and their changing trends were the same, thus jointly affecting urban expansion. The correlation coefficient between urban compactness and nighttime lights was close to −1, and their changing trend was opposite, thus affecting urban expansion in the opposite direction. The correlation coefficient between passenger transport capacity and nighttime lights was close to 0 and their trend similarity was low, which had a small impact on urban expansion.
In urban environmental parameters, the correlation coefficient between the production of general industrial solid waste and nighttime lighting was 1 and their changing trends were the same, thus jointly affecting urban expansion. The correlation coefficient between exhaust emissions, sewage treatment rate, parks and green areas, and nighttime lights was close to 1 and their changing trends were the same, thus jointly affecting urban expansion. The correlation coefficient between household consumption of LPG, wastewater discharge, and nighttime lights was close to −1 and their changing trend was opposite, thus affecting urban expansion in the opposite direction. The correlation coefficient between the number of key pollution control projects and nighttime lights was close to −0 and their trend similarity was low, which had a small impact on urban expansion.
In order to explore the degree of coordination in urban development, this article selected 17 indicators possessing a high correlation coefficient with nighttime lights from two aspects for analysis, explored the trends in changes between urban expansion and the urban environment, and provided suggestions for urban development.
4.2.1. Weight Analysis
The remote sensing images applied in this paper for extraction of built-up areas, combined with urban parameter indicators, build an evaluation index system for compositing the degree of coordination in urban expansion and the urban environment; the entropy method was applied in the calculation of a standardized matrix [
43,
44,
45,
46], information entropy, and feature weight. The formula for calculating the entropy method is shown in
Section 3.4. The calculated feature weights are listed in
Table 5.
As shown in
Table 5, it can be seen that among the urban development factors, GDP per square kilometer of built-up areas had the highest weight, accounting for 21%, thus indicating it to be the most important factor affecting urban development. Second was compactness and third was drainage facilities, followed by road density, student density in urban built-up areas, and electricity consumption. The factor with the lowest proportion was population density in built-up areas, which was only 7%, thus having a relatively small impact on urban development.
Among urban environmental factors, the proportion of green spaces in built-up areas had the highest weight, accounting for 18.8%, thus indicating that it was the most important factor affecting the urban environment. Second was exhaust emissions and third was the production of general industrial solid waste, followed by wastewater emissions, average noise value, and sewage treatment rate. The minimum weight proportion was found for soot emissions, which was only 11%, thus having a relatively small impact on the urban environment.
4.2.2. Trend Analysis
To explore changes in the degree of coordination over time, this article used analysis of changes in the degree of coordination to analyze trends [
47,
48]. The formula for the coordination index trend model is shown in
Section 3.5. The results are shown in
Table 6.
From 1995 to 2020, the macro and micro changes in the coordination index between urban development and environmental parameters showed a downward trend, which indicates that the coordination between urban development and environmental parameters had been improving. At the same time as urban development, environmental parameters had been relatively effectively controlled. Compared with the long-term coordination curve, the short-term coordination curve exhibits more frequent and significant changes. There was no long-term planning for urban development and environmental parameters, and environmental parameters were arbitrary and lacked supervision.
From 1995 to 2020, the trend in changes according to the macro model of the coordination index was 0.847, indicating that the degree of coordination between urban development and environmental pollution in 2020 was 84.7% compared to 1995, indicating an overall improvement in the degree of coordination.
From 1995 to 2000, the trend in changes according to the micro model of the coordination index was 0.932, indicating an improvement in the degree of coordination. From 2000 to 2005, the trend for changes in the coordination index was 1.027, indicating a deterioration in coordination. From 2005 to 2010, the trend for changes in the coordination index was 0.85444, indicating an improvement in coordination. From 2010 to 2015, the trend for changes in the coordination index in Nanjing was 1.213, indicating a deterioration in coordination. From 2015 to 2020, the trend for changes in the coordination index was 0.843, indicating an improvement in the degree of coordination.
4.2.3. Impact Factor Analysis
In order to explore the factors that have the greatest impact on environmental change, this article used the obstacle model and the minimum variance model to calculate the factors that have the greatest impact on the environment during each period [
49,
50,
51,
52]; the formula for obstacle factor analysis is shown in
Section 3.6, and the results are shown in
Figure 8.
Comparing the number of occurrences for the seven indicators, sewage treatment rate only became a source of obstacles twice and had the smallest impact on co-scheduling. Average noise value, wastewater discharge, and soot emissions each became obstacle sources three times, with a moderate impact on coordinated scheduling. The amount of exhaust emissions and the production of general industrial solid waste each became obstacles four times, which had a significant impact on coordinated scheduling. The proportion of green spaces in built-up areas became a source of obstacles five times, thus having the greatest impact on coordinated scheduling and highlighting it as an important factor causing disharmony.
In 1995, three indicators became obstacles to urban environmental development, with average noise value being the largest indicator, followed by wastewater discharge and sewage treatment rate.
In 2000, five indicators became obstacles to urban environmental development, with the proportion of green spaces in built-up areas being the largest indicator, followed by wastewater discharge, sewage treatment rate, average noise value, and soot emissions. Comparing the five obstacle indicators for the year 1995, the proportion of green spaces in built-up areas had decreased by 20%, wastewater discharge had decreased by 27%, sewage treatment rate had increased by 70%, the average noise value had decreased by 2%, and soot emissions had increased by 3%.
In 2005, xix indicators became obstacles to urban environmental development, with the production of general industrial solid waste being the largest indicator, followed by average noise, wastewater discharge, exhaust emissions, soot emissions, and the proportion of green spaces in built-up areas. Comparing the five obstacle indicators for the year 2000, the production of general industrial solid waste had increased by 77%, the average noise value had increased by 2%, wastewater discharge had decreased by 27%, exhaust emissions had increased by 74%, soot emissions had decreased by 7%, and the proportion of green spaces in built-up areas had increased by 23%.
In 2010, three indicators became obstacles to urban environmental development, with the production of general industrial solid waste being the largest indicator, followed by exhaust emissions and the proportion of green spaces in built-up areas. Comparing the three obstacle indicators in 2005, the production of general industrial solid waste had increased by 42%, exhaust emissions had increased by 52%, and the proportion of green spaces in built-up areas had decreased by 5%.
In 2015, four indicators became obstacles to urban environmental development, with exhaust emissions being the largest indicator, followed by the proportion of green spaces in built-up areas, the production of general industrial solid waste, and soot emissions. Comparing the four obstacle indicators in 2010, exhaust emissions had increased by 53%, the proportion of green spaces in built-up areas had decreased by 10%, the production of general industrial solid waste had decreased by 10%, and soot emissions had increased by 149%.
In 2020, three indicators became obstacles to urban environmental development, with exhaust emissions being the largest indicator, followed by the production of general industrial solid waste and the proportion of green spaces in built-up areas. Comparing the three obstacle indicators in 2015, exhaust emissions had increased by 6%, the production of general industrial solid waste had increased by 8%, and the proportion of green spaces in built-up areas had increased by 2%.
4.2.4. Comparative Analysis of Cities
The research results obtained in this article were similar to Chen’s [
53] research showing that the coordination between urban development and environment parameters in Nanjing had improved. However, the current environmental parameters still posed a challenge for achieving the goals of energy conservation, emissions reductions, and carbon peaking in Nanjing by 2030. The results obtained in this article were similar to Lu’s [
54] and Lei’s [
55] research, where solid waste and green spaces were the main obstacles to environmental development.
In order to conduct a deeper analysis of the two environmental obstacle sources in Nanjing, identify the differences between Nanjing and other cities, and provide suggestions for future development, this article selected 15 cities with similar urban scale levels to Nanjing and compared the production of general industrial solid waste and the proportion of green spaces in built-up areas for these cities in a time series. The comparison results are shown in
Figure 9,
Figure 10,
Figure 11 and
Figure 12.
As shown in
Figure 9, according to the 25-year average for the production of general industrial solid waste, Nanjing was the highest. In each analysis period, except for 2015, Nanjing ranked at the highest level in terms of the production of general industrial solid waste.
SLOPE analysis was conducted on changes in the production of general industrial solid waste in the 16 cities; the formula for linear propensity estimation is shown in
Section 3.7, and the analysis results are shown in
Figure 10.
As shown in
Figure 10, according to the results of trend analysis, Nanjing had the highest growth trend for the production of general industrial solid waste among the 16 cities. Compared to other cities, Nanjing had a higher growth rate for the production of general industrial solid waste and a larger growth volume.
As shown in
Figure 11, among the 16 cities, the proportion of green spaces in built-up areas in Nanjing is at an average level, ranked sixth on average over the past 25 years. In 1995, the proportion of green spaces in built-up areas in Nanjing ranked third, while in 2020 it ranked second from last.
As shown in
Figure 12, according to the trend analysis results, Nanjing has the lowest trend among the 16 cities for the proportion of green spaces in built-up areas. Compared to other cities, the proportion of green spaces is increasing over time, while Nanjing shows a decreasing trend.
In general, compared to other cities of the same level, Nanjing had more production of general industrial solid waste and a larger growth trend. In terms of the proportion of green spaces in built-up areas, Nanjing did not occupy a leading position and had a slow decreasing trend. They were all obstacles to the healthy development of Nanjing city.