1. Introduction
Since the 1980s, global changes, marked by global warming, have gradually been attracting significant attention worldwide [
1]. Since climatic changes have considerable impacts on natural ecosystems and economic systems [
2], coping with the impacts of global warming has become an important issue for all countries striving for sustainable development [
3]. Against this background, measures to reduce greenhouse gas emissions are becoming increasingly important for the development of a sustainable industrial economy. Research on the impacts of climate change on the industrial economic system can directly or indirectly affect the sustainable development of global and regional economies as well as the development of climate adaptation measures and emission reduction standards. Hence, systematic research in this field is of great significance.
Among numerous socio-economic sectors, the industrial sector is highly sensitive to climatic changes, and its sensitivity ranks only behind that of the agricultural, aviation, and construction industries [
4]. The impacts of climate change on the industrial sector are generally manifested in two aspects. The first one is the direct impact of climate change, especially in the form of extreme weather events, on industrial production [
5]. Most of these impacts are negative, and extreme weather events can directly lead to infrastructure damage, production halting, and casualties in the industrial sector. The second aspect refers to the fact that climatic factors do not directly affect industrial production, but have potential indirect impacts on the industrial sector (e.g., processing industry, manufacturing industry) through agriculture and animal husbandry [
6,
7]. For example, climatic changes can result in increased or decreased agricultural production, thus affecting the price of agricultural products and, consequently, the costs of the processing and manufacturing industries that use these agricultural products as main raw materials. From another angle, demand and supply changes may also affect the industrial economy. For example, temperature rises can lead to a higher demand for air conditioners, electric fans, and cold drink products, which will promote the mass production and processing of such products. Several studies [
8,
9,
10,
11] have shown that, on a global level, extreme climatic events, such as heavy precipitation, drought, extremely high and low temperatures, have great positive and negative impacts on industrial economic systems, although the adverse impacts are dominant. Against the background of the predicted continuous climate warming in the future, industrial production will face several obstacles such as intensified resource exhaustion and frequent extreme climate events, resulting in a greater overall sensitivity to climate change.
At present, the exposure degree assessment on climate change mainly focuses on agriculture, forestry, husbandry, and the fishery industry [
12]. Climatic changes can have direct impacts on the soil for agricultural production, resulting in altered crop growth. Related studies have shown that under the impact of climate change, the soil organic carbon (SOC) content in the top soil layer of Mediterranean agricultural areas generally decreases, while that in the lower soil layer generally increases [
13]. For example, Awoye et al. [
14] have investigated the impacts of climate change on the yields of different crops in western African countries and found that such changes could lead to a severe yield reduction of 11–33% for pineapple, corn, peanuts, cassava, and cowpea; whereas an average yield increment of 10–39% was calculated for sorghum, yam, cotton, and rice. At the same time, climatic changes can also have a serious impact on agricultural practitioners. Some studies have shown that due to the impact of climatic changes, the prevalence of diseases related to ultraviolet radiation and heat stress among agricultural workers presents an aggravating trend [
15].
Although numerous studies have focused on the exposure degree of the primary industry [
16,
17,
18], research on the exposure assessment of the secondary and tertiary industries is scarce. In the studies that identified the impacts of climate change on the industrial economic system, the core step is to conduct spatial superposition analysis on climate factor data and industrial data with the same spatiotemporal resolution [
19]. However, at present, studies assessing industrial economic data mostly take the administrative divisions of cities and counties as the research unit, and such data can only be obtained in the form of average statistical data or aggregate statistical data, which cannot be matched with climate data on a spatial level. At the same time, the industrial economic data stored in the form of aggregate and average data cannot reflect the spatial heterogeneity of the industrial industry, resulting in a poor accuracy of climate risk assessments. Through quantitative description of the spatial distribution rules of grid-scale industrial economic data and the characteristics of grid-scale climatic changes, the mechanisms underlying the impacts of climate change on the industrial economic system can be revealed, thereby promoting research on the industrial economy against the background of climatic changes and climate risk assessment and providing a scientific basis for the proposal of policies to adapt to climatic changes and to mitigate their effects.
At present, research methods for the spatialization of economic data can be divided into three categories, i.e., the spatial interpolation model [
20,
21], the multi-source data fusion model [
22,
23], and the remote sensing inversion model [
24]. Due to the premise of uniform distribution of social and economic data, the spatial interpolation model is mostly used in small-scale studies with large sample numbers, relatively flat terrain, and no other auxiliary data, whereas it is rarely used in large-scale studies where spatial changes are complex and data sampling is difficult. This is mainly because industrial output value data are generally stored in administrative regions such as provinces, cities, and counties. The spatial resolution is extremely low, and the spatialization accuracy of the spatial interpolation model is extremely poor, making it necessary to increase the number of sampling points; however, gathering statistics is difficult, and human, material, and financial resources are huge. The multi-source data fusion model aims to obtain accurate dependent variable results through correlation analysis or other analysis of multiple covariables. However, due to the autocorrelation between covariables and the heterogeneity of geographic space, the model should be implemented in partitioned subregions when large-scale regions are studied, which means that it has a poor universality. Due to the development of spatial data and remote sensing images [
25,
26], the multi-source fusion model is widely used. Compared with the spatialization by the interpolation model and the multi-source data fusion model, the nighttime light remote sensing data inversion model has a simple operation method, high accuracy, and spatiotemporal characteristics, potentially solving the problem of the spatialization of large-scale data. However, at present, this model is mainly applied in the analysis of GDP (Gross domestic product) and population data. In terms of industrial economy, due to the difficulty in the collection of statistical data, spatialized datasets for country-level and global industrial economy—especially spatialized datasets for the industrial economy under future climate scenarios—are scarce, impeding the risk assessment of the industrial economy.
In this context, the research objectives of this paper are as follows: (1) to explore the spatial distribution characteristics of industrial output at the current stage by taking mainland China as an example; (2) to analyze the changes in the industrial output under global warming scenarios RCP4.5 and RCP8.5, based on the random forest algorithm in machine learning.
3. Research Results
3.1. Analysis of CMIP5 Multi-Mode Coupling Results
In this study, 26 sets of climate modes were extensively selected based on the current climate modes and climate scenarios in CMIP5; subsequently, five optimal climate modes (i.e., CMCC-CM, MIROC5, MIROC-ESM, MRI-CGCM3, and MPI-ESM-LR) were selected according to the simulation capabilities and differences of all modes for different climate scenarios to predict air temperature and precipitation during 2000–2050 under different climate change scenarios in China.
According to the
RMSE and relative
RMSE methods, we verified the simulation accuracy of different climate modes by using the actual values of meteorological site data in China in 2000. We then obtained the simulation deviations of China’s air temperature and precipitation data in 2000 for all modes and after multi-mode ensemble average operation, as shown in
Table 3.
In general, the five modes selected in this paper can well simulate the air temperature and precipitation status in China (
Table 3). In terms of air temperature simulation, CMCC-CM, MIROC5, MPI-ESM-LR, and MME showed good simulation capabilities. In the simulation of precipitation, the simulation capability of each mode was similar. Therefore, we considered the simulation capabilities of all modes for air temperature and precipitation and finally selected the multi-mode ensemble (
MME) average results with strong simulation capabilities for both air temperature and precipitation as the temperature and precipitation data for the subsequent study.
The external forcing used in the CMIP5 model is the typical concentration path (RCP), which is a future emission scenario simulated based on many assumptions for future development. There are four types of scenarios: RCP8.5 is a higher emission scenario, where emissions continue to increase, and radiative forcing will rise to 8.5 w/m2 by 2100; RCP6.0 and RCP4.5 are medium emission scenarios, and radiative forcing by 2100 will be 6.0 w/m2 and 4.5 w/m2, respectively; RCP2.6 is low emission scenario; the radiative forcing under this scenario first increases and then decreases, with a decrease to 2.6 w/m2 by 2100.
Regarding the choice of the emission scenario mode, based on the literature research [
34,
35] and the actual global CO
2 emission level, we assume that the premise of the RCP2.6 scenario is a change in the type of energy use on a global scale. It is an ideal scenario, albeit difficult to achieve. Both RCP4.5 and RCP6.0 are emission scenarios under government intervention and belong to medium emission scenarios. Among them, RCP4.5 is the most widely used scenario and more likely in the future, whereas RCP8.5 is an extreme scenario, which is a baseline scenario without climate change policy intervention. Therefore, we selected the more likely medium-emission-intensity RCP4.5 emission scenario and the worst-case high-emission scenario RCP8.5 for future scenario analysis, corresponding to SSP2 and SSP5 in CMIP6 mode.
3.2. Spatialization Feature Analysis of the Current Industrial Output Value
We analyzed the overall spatial distribution features of the industrial output value by kernel density estimation. In terms of bandwidth selection, after numerous attempts and based on literature studies, we selected 100 km as the bandwidth to conduct spatial kernel density analysis on China’s industrial output value from 2000 to 2010. In the selection of weights, because it is impossible to determine whether the industrial output value of each point comes from a certain industry, we do not distinguish the population field. The default is therefore ‘none’, which means the default count, that is, the weight of each point, is equal to 1 (as shown in
Figure 3).
The simulation results (
Figure 3) show that the kernel density of China’s industrial output value presented an increasing trend from 2000 to 2010. Especially in the Yangtze River Delta, the Pearl River Delta, and the Bohai Rim region, this trend was significant; at the same time, the kernel density values in some regions of Henan, Hubei, Hunan, Sichuan-Chongqing economic zone, Jilin, and Liaoning also increased significantly. These regions have suitable geographical conditions and abundant natural resources; because of the Yangtze River and the Bohai economic rim, transportation is highly convenient. Therefore, the industry has well developed in the past decade, and the industrial development presents an expanding trend, with the formation of several industrial parks. From 2000 to 2010, China’s industrial output value increased significantly, along with the area of industrial land. At the same time, the spatial distribution of industrial output became more concentrated, with the formation of numerous intensively distributed industrial zones. Even in northern Xinjiang, Inner Mongolia, Heilongjiang, Yunnan, Guizhou, and most regions of Guangxi, the kernel density values showed an obvious increase of 0–10/km
2. However, in southern Xinjiang, some regions of Tibet, and southern Qinghai, the overall output values were relatively low, and the kernel density values of some regions even showed a decreasing trend. The main reason for such a development is that these regions are located inland, with inconvenient traffic conditions and relatively poor terrain conditions, leading to a patchy industrial distribution and, consequently, slow industrial development.
3.3. Industrial Output Spatialization under Scenarios RCP4.5 and RCP8.5
The average accuracy of the industrial output value obtained by the random forest simulation was 93.77%, which proves that the random forest model has a high simulation accuracy and can be used to study industrial output spatialization under different climate change scenarios. According to the climate factor data in China and the random forest model under different scenarios, the spatial changes of the proportions of China’s industrial output under the climate scenarios RCP4.5 and RCP8.5 from 2010 to 2050 were analyzed (
Figure 4 and
Figure 5).
As sown in
Figure 4a–c, the proportions of the industrial output under scenarios RCP4.5 and 8.5 are lower than that of the current industrial output value; especially in the central, eastern, and southern coastal regions, the proportion of the industrial output showed a significant decrease.
According to
Figure 4 and
Figure 5, the proportions of the industrial output values under different climate change scenarios were different. Overall, the regions with higher proportions of the industrial output value were mainly distributed in the Bohai Rim region, the Yangtze River Delta coastal region, and the Pearl River Delta region, while the proportions of the industrial output value in the northwestern region were generally low. Under scenarios RCP4.5 and 8.5, the proportions of the industrial output value showed a declining trend from 2010 to 2050. For industrial land, from 2010 to 2050, the proportion of industrial land is predicted to show an increasing trend. In 2030, the predicted proportion of industrial land increases by 1.37 ‰ compared with 2010. In particular, industrial land in the Beijing-Tianjin-Hebei region has changed significantly, mostly characterized by an increase, whereas the middle and lower reaches of the Yangtze River will mainly show a decrease. By 2050, the proportion of industrial land will be increased by 1.22 ‰ compared with 2030. By this time, the industrial land in the Beijing-Tianjin-Hebei region will have changed significantly, showing a significant decrease, but other areas around the Bohai Bay will show an increasing trend. The middle and lower reaches of the Yangtze River will mainly show a decreasing trend, along with the Pearl River Delta.
Under scenario RCP4.5, the industrial output value of the kilometer grid shows a trend of decreasing year by year. Especially from 2030, the industrial output value grid is predicted to change greatly, and the high-value areas will be significantly reduced, especially in the central and eastern plains, with obvious changes on a yearly basis. Also, from 2030, the low value of the Bohai Rim will decrease significantly, and the distribution will tend to be far away from the central area of Beijing. The Yangtze River Delta will show a reduced area of industrial land from 2030, and the industrial output value will also show a downward trend. The high value of the Pearl River Delta will remain relatively unchanged, although the distribution range will be reduced.
Under scenario RCP8.5, the industrial output value of the kilometer grid will show a downward trend on a yearly basis, but the total industrial output value will be slightly higher than that in scenario RCP4.5; the overall change trend will be similar to that under scenario RCP4.5. From 2030, the distribution area of the Bohai Rim will decrease and will be located further away from the central area of Beijing and the coastal areas. Compared with the RCP4.5 scenario, the output value of each grid will decrease. The industrial output value of the Yangtze River Delta and the Bohai Rim will also decrease on a yearly basis while moving toward the coast; the output value of each grid will be lower than that under scenario RCP4.5.
By vertically comparing the proportions of China’s industrial output in different years (
Figure 5a–d), we observed that the proportions of the industrial output under scenarios RCP4.5 and RCP8.5 showed a significant decline; especially in 2050, the proportion of the industrial output value presents a significantly decreasing overall trend when compared with that in 2010. In 2010, the proportion of the industrial output value under scenario RCP4.5 ranged between 0 and 15.148‱, and that under RCP8.5 ranged between 0 and 14.984‱. By contrast, in 2050, the proportions of the industrial output under scenarios RCP4.5 and RCP8.5 decrease by 0.352 and 0.352‱, respectively.
3.4. Annual Change Analysis of Industrial Output under Different Climate Scenarios
The greatest contribution of radiative forcing comes from the increasing CO
2 concentration in the atmosphere. Under scenario RCP4.5, radiative forcing will stabilize at 4.5 w/m
2 after 2100, and the CO
2 emission concentration will reach 850 mL/m
3. By contrast, under scenario RCP8.5, radiative forcing will stabilize at 8.5 w/m
2 after 2100, and the CO
2 emission concentration will reach 1370 mL/m
3. Previous studies have shown that the main cause of climatic changes is the burning of fossil fuels. In particular, CO
2 generated in industrial processes is an important cause of climate change. Under scenarios RCP4.5 and RCP8.5 (
Table 4), since the industrial output value is positively correlated with CO
2 concentration, to some extent, the total industrial output value also presents a relatively obvious increasing trend with increasing CO
2 concentrations. However, under scenarios RCP4.5 and RCP8.5, climatic changes will greatly fluctuate. From 2010 to 2050, the air temperature under scenarios RCP4.5 and RCP8.5 is predicted to increase by 1.47 and 2.19 °C, respectively, whereas precipitation is predicted to increase by 24.58 and 17.53 mm, respectively. At the same time, the frequency of extreme weather events will most likely increase. Climatic changes under different scenarios have certain impacts on the change of the industrial output value. The annual average growth rate of industrial output from 2010 to 2020 under scenarios RCP4.5 and RCP8.5 is predicted to be 7.988% and 7.256%, respectively, while the average annual growth rate of industrial output from 2030 to 2050 is predicted to decrease to 0.679% and 0.721%, respectively.
4. Conclusions and Discussion
Global climatic changes have become one of the key issues in many scientific fields. As a pillar industry of the national economy, the industry should be responsible for climatic changes, while it is also greatly affected by such changes. In this paper, taking mainland China as an example, we obtained the spatialized datasets of the current industrial output from remote sensing retrieval. By combination with the results of CMIP5 multi-mode climate coupling, we constructed the future spatialized model of industrial output values under different climate scenarios, based on the random forest method in machine learning. This model breaks the limit of administrative boundaries and can intuitively analyze the quantitative differences and the spatiotemporal distribution characteristics of industrial output on the grid scale. Our results provide data support for the classification of key industrial regions and the assessment of industrial land efficiency in China under different climate change scenarios. In particular, these results lay a foundation for the assessment of exposure degree, vulnerability, and risk of the industry against the background of climate change.
Our study shows that, under the two higher emission intensities (RCP4.5 and RCP8.5), the population is large, the science and technology innovation is sluggish, and the energy and resource use efficiency is low [
36]. As a consequence, the industrial output value increases slowly. In particular, the average proportion of the industrial output value presents a sharply decreasing trend under industrial land expansion and a slow growth of industrial output, indicating the unbalanced development of the industrial output value. Based on the overall development of industrial output throughout China, the regions with higher proportions of the industrial output value are concentrated in the Bohai Rim region, the Yangtze River Delta coastal region, and the Pearl River Delta region, where the land area is small and the population and resources are highly concentrated. However, in the northwestern regions, with a large land area and a sparse population, the proportion of the industrial output value is generally low. In combination with regional development plans, these findings lay a solid foundation for the future adjustment and optimization of industrial layout and structure, strictly controlling the implementation of projects with high pollution and high energy consumption and promoting the development of clean-energy industries and new technological and low-carbon industries.
Exposure of the vulnerability of the industrial economic system, through risk assessment and cost-benefit analysis, is an effective way to further reveal the impacts of climate change on the economic development of industrial production, a pillar industry in China. At the same time, with binding greenhouse gas emission reduction obligations, actively adjusting the industrial economic structure and industrial layout, strictly controlling the implementation of high-pollution and high-energy-consuming projects, and actively promoting the development of clean energy industries can effectively guarantee the improvement of the industrial economy.
Our study has some limitations, however, which require further work:
Land use data are emphasized in this article. In the spatialization of industrial output value, all industrial output values were generated for industrial land. Industrial land data are generally current or past industrial land monitoring data, mostly generated by land use simulation methods. However, CMIP5 has achieved 2100 in different climate change scenarios. If the future industrial land in this model is replaced with the updated, predicted industrial land monitoring data, the spatial accuracy of industrial output value will be improved.
Although we selected numerous influencing factors for the spatial analysis of the industrial output value, more factors exist, such as policy influence, fixed asset input, and laborer health degree. However, because of the lack of such data, these factors are not considered here. At the same time, in the spatialized model of industrial output value under different climate scenarios, the possibility of changes in the influencing factors is considered; for example, changes in the elevation may affect plant site selection, but it is difficult to obtain such data, especially in the context of future changes. However, we stress that this paper focuses on the effects of climate change, and therefore, other indicators are assumed to remain unchanged. In the future, relevant uncertainty research should, however, be considered.