Low-Carbon Development from the Energy–Water Nexus Perspective in China’s Resource-Based City

Abstract

Energy crises, water shortages, and rising carbon emissions are constantly posing new demands and challenges to global economic development. Considering the problem of high emissions and high water consumption in the process of energy production and transformation in resource-based cities, this study established the LEAP-Jincheng model based on the low emissions analysis platform (LEAP) model. Taking 2020 as the base year, the baseline scenario (BS), policy scenario (PS), and intensified scenario (IS) were set to predict future energy and water consumption and carbon emissions of Jincheng from 2021 to 2050. The results show that both PS and IS can achieve energy conservation and emission reduction to some extent. The total energy consumption of PS will be 32.89 million metric tons of coal equivalent in 2050, 15.62% less than the BS. However, the carbon emissions in 2030 will reach 8221 metric tons CO₂ equivalent, which is significantly higher than that in other scenarios. In PS, carbon emissions after 2030 will not be significantly reduced, and the energy–water elasticity coefficient is −0.77, which fails to achieve effective emission reduction under energy–water synergy. The total energy consumption of the IS will be 22.57 million metric tons of coal equities in 2050, which has a total decrease of 31.38%, compared to BS. In the IS, the carbon emissions will reach a peak in 2030 (68.77 million metric tons CO₂ equivalent) and subsequently reduce to 50.72 million metric tons CO₂ equivalent in 2050, which has a total decrease of 50.64%, compared to BS. Furthermore, water consumption and energy–water synergy results show that the elastic coefficient is 1.37 in the IS. The IS is the best scenario for Jincheng to achieve coordinated development of energy and water resources from a low-carbon perspective. This study can provide a scientific basis for decision-making departments of Jincheng to formulate targeted sustainable development policies for energy and water and has an essential promoting significance for China to achieve the “double carbon” goals.

1. Introduction

With the development of the economy, the world is facing the challenges of climate change, energy crisis, and water shortage [1,2]. At present, surface temperatures are rising approximately 0.2 °C per decade. Carbon emissions from burning fossil fuels are one of the main drivers of rising global temperatures [3]. To address climate change, the Paris Agreement unified arrangements for worldwide action against climate change and set the goal of the future climate change target (1.5 °C) in 2015 [4,5,6]. Subsequently, many countries have developed carbon neutrality targets. In 2020, China incorporated the goal of addressing climate change into the 14th Five-Year Plan. In September of the same year, China reiterated its commitment at the United Nations General Assembly to ”strive to achieve carbon peak in 2030 and carbon neutrality in 2060”, which guides China’s green and low-carbon energy development [7]. At the same time, the energy sector is the second largest water consumption sector after agriculture. As a necessity for energy exploitation, processing, and use, the increase in energy demand will further aggravate the pressure on the water resources system. It is estimated that two-thirds of the world’s population will face water shortages by 2025 [8,9,10]. In this context, the contradiction between the rising greenhouse gas emissions caused by energy and water consumption and the global climate change targets becomes more apparent. According to the 2017 BP World Energy Outlook released by the International Energy Agency (IEA), the global demand for water in energy production is enormous, and its growth rate is about twice that of global energy demand. The amount of water used in energy production is expected to increase by at least 20 percent by 2035. As a region where human social activities are concentrated and distributed, the development of the water–energy system in cities plays a crucial role in global climate change [11,12], especially for resource-based cities, these cities carry more energy industrial production, such as power generation, coal development, and so on. The study [13] shows that water consumption in the global energy system could be reduced by 37–66% by 2030, compared to 2012, by improving the water use efficiency of energy production technologies. Therefore, the collaborative optimization of water–energy systems in resource-based cities is necessary. This paper selects representative resource-based cities for research. The question of how to identify the production and flow relationships among water resource use, energy consumption, and carbon emissions at the city level is the key to achieving efficient resource utilization, energy conservation, and “double carbon” goals [14].

Presently, relevant researchers have accumulated many theories and experiences of water–energy synergy [15,16,17]. Based on process-based life-cycle analysis (LCA) and input–output analysis (IOA), Feng et al. [18] evaluated the water use of eight power generation technologies. The results showed that, compared with the current fuel structure and power generation technology, the transformation to low-carbon renewable power generation technology can save more than 79% of the total carbon dioxide emissions in the life cycle and more than 50% of the water consumption per kWh. According to the survey data of villages in eleven provinces of China, Wang et al. [19] explored the water–energy relationship of agriculture. They evaluated the carbon emissions of groundwater irrigation and the importance of energy use. The results showed a great potential benefit to water and energy conservation. Qin et al. [20] analyzed the water withdrawals and consumption in the energy processes and compliance with the “3 Red Lines” policy. They analyzed the common interests and balanced the relationship between the energy and water system policies. The results showed that water consumption in the energy sector is highly dependent on technology choices, especially cooling technologies for power plants, while future high electricity demand is mainly met by coal and nuclear power. Valek et al. [21] analyzed the relationship between water and energy in Mexico City and its impact on climate change, and assessed the carbon emissions associated with the water system. The results show that water supply accounts for 90% of the system’s energy consumption, and water-saving measures will reduce energy use. Yang et al. [22] explored the relationship between energy, water, and carbon at the urban scale. They estimated the energy consumption, water consumption, and carbon emissions of various sectors in Shanghai and Beijing using the environmental input–output model. Furthermore, they proposed sustainable development suggestions for different industries based on the energy–water–carbon relationship. The results showed that the environmental pressure per unit output in Shanghai is higher than in Beijing, which provided a new perspective for solving environmental challenges in realizing urban sustainability.

Although the current researches on energy and water resources play an important guiding role in saving energy and water and mitigating climate change, most of these studies were concentrated in a few specific industries. There is a lack of emission accounting in all sectors, and there is little research on the coordinated development path of energy and water from the perspective of low carbon. On the scale of the study, the research was generally carried out on the provincial or regional scale, and research at the city scale is insufficient, especially for resource-based cities with large energy production and water resource shortages. Given these problems, this study takes Jincheng (a resource-based city with the characteristics of high energy exploitation) as an example to predict energy demand, water consumption in energy transformation, and carbon emissions. The purpose of this study is to construct an assessment method and technology for the coordinated development of energy and water resources covering all sectors at the urban scale from the perspective of low carbon and to explore the path of low-carbon development under the coordination of energy and water resources.

The low emissions analysis platform (LEAP) model is a bottom-up model with a flexible data structure. By adjusting demand-side parameters, it can predict medium and long-term energy supply and demand and environmental impacts in different scenarios [23,24,25]. This model can simulate different scales from department to country, realize energy and carbon emissions under multi-scenarios, and determine how energy flows across sectors [26,27]. In terms of the studies of water–energy correlations, Zhou et al. [28] adopted the LEAP model to analyze the impact of energy policies on water resources in the Jiangsu province and calculated the water resources saved in different energy scenarios. The results show that a shift in cooling technology can have significant water-saving effects. Meanwhile, more efforts are needed to offset the negative impacts of environmental change on energy and water resources in the case of reduced power output efficiency and increased water consumption.

As an important coal city in the energy revolution, Jincheng is facing more opportunities and challenges in coordinating energy and water resource development [29]. This study takes Jincheng as an example to explore the problem of water consumption in the energy production process at the urban scale. By accounting for energy consumption and carbon emissions in Jincheng from 2010 to 2020, energy flow and water consumption in the energy production and conversion sector were mastered. LEAP-Jincheng model was established based on the LEAP model. Taking 2020 as the base year, the baseline scenario (BS) was set based on the forecast trends of the economy and demographic. The policy scenario (PS) was set by constraining the government planning documents. The intensified scenario (IS) was set with the goal of strong energy conservation and accelerated industrial adjustment. Then, carbon emissions, water consumption, and energy–water synergy in different scenarios were calculated to explore a low-carbon development path suitable for Jincheng.

The innovation and purpose of this study is the exploration of the path of the coordinated development of energy and water resources from the low-carbon perspective on the urban scale using a multi-scenario analysis. An accounting method system covering all industries of energy and water resources coordinated development at city scale is proposed. This study provides a path for the coordinated development of energy and water resources for Jincheng and other resource-based cities from the perspective of low carbon and provides effective technical methods and data support for further research by other scholars in this field. This study is of great significance for China to achieve the “double carbon” goal.

2. Data Source

The energy consumption and production data of various energy types and sectors from 2010 to 2020 were obtained from the Jincheng Statistical Yearbook [30] and Shanxi Statistical Yearbook [31]. Water consumption was obtained from the Jincheng Water Resources Bulletin [32]. This study took 2020 as the base year, and the forecast years were 2021 to 2050 due to the time accuracy. In the scenario setting, to better reflect the energy growth trend excluding policies and technological innovation in the BS, key assumptions, such as economy, population, and urbanization rate, were based on the report of The Long View: How will the global economic order change by 2050 [33]. This report was published by Price Waterhouse Coopers (PwC), using a long-term economic growth model to estimate the potential long-term growth rates of countries around the world, considering demographic, capital, education, technology, and other trends rigorously. Data on energy intensity and future goals set in other scenarios are summarized according to relevant policies and regulations can be found in Section 5.2. The water consumption rate data were obtained from Shanxi Province Water Consumption Rules [34], and some missing values came from the water consumption rules by other provinces. The emission factor was based on the built-in IPCC emission factor in LEAP.

3. Overview of the Study Area

Jincheng is in the southeast of Shanxi Province (Figure 1), covering an area of 9490 km². It is located at the southern end of the “Qinshui” coalfield. The coal-bearing area of Jincheng is 5350 km², accounting for 56.4% of the total area. The total reserves of anthracite are 80.8 billion tons, accounting for more than a quarter of China [35,36]. The proven reserves of coalbed methane are 100 billion m³.

The situation of energy and water in Jincheng from 2010 to 2020 was analyzed [30]. With the development of cities, energy consumption has more than doubled in a decade, rising from 233.70 million GJ in 2010 to 545.75 million GJ in 2020. The industry is the primary energy consumer, industrial consumption accounts for more than 70% each year, which shows an increasing trend, and reaches 90% in 2019. (Figure 2A). Energy production is also on the rise. Compared with 2010, natural gas, electricity, and coal increased by 130.96%, 34.76%, and 44.41%, respectively, in 2020. Among them, natural gas production has shown a continuous growth trend, reaching 4.98 billion m³ in 2020, which cannot be separated from the government’s preferential policy on natural gas development. Electricity and natural gas production will continue to grow depending on future development and planning (Figure 2B). Industry and agriculture are the main water consumption sectors in Jincheng, showing a trend of fluctuation and decline. Although industry used more energy in 2020, it reduces water consumption by 34.64 million m³ [32]. It shows that the improvement of water-saving policies and technologies positively affects water utilization (Figure 2C). In the industrial sector of resource cities, water use in energy production and conversion has a big difference from other cities. In this study, water consumption in energy production and conversion is divided, predicted, and set according to the water quota of Shanxi Province (Table 1). In previous studies [37], water used for hydropower mainly came from evaporation, which could not be counted in detail. Water used for photovoltaic power generation mainly came from equipment cleaning, so the two were no longer considered.

4. Methods and Data Source

4.1. Research Framework

In this study, the following steps were used to analyze the low-carbon development path of energy and water resources in Jincheng (Figure 3).

Step 1: Current situation investigation. Based on the energy, water, economic, and social data of Jincheng, as well as the multi-level energy policies of the state, Shanxi Province and Jincheng City, the energy production, energy consumption and water consumption in Jincheng from 2010 to 2020 were analyzed in this study, and the problems existing in the coordinated development of energy and water resources in Jincheng were explored.

Step 2: Establish the LEAP-Jincheng model. Based on the analysis of the driving factors affecting the energy demand and consumption of all industry in Jincheng, the LEAP-Jincheng model was established, and the multi-scenario was set with 2020 as the base year to predict the energy demand and carbon emissions of various industries from 2021 to 2050 in Jincheng.

Step 3: Analysis of synergy of energy and water resources. The water consumption in the energy production and conversion of each scenario was calculated, and the elastic coefficient was used to calculate the synergy of energy and water resources of different scenarios in Jincheng.

Step 4: Policy implications. Based on the prediction results of energy demand, carbon emissions, and water consumption, as well as the analysis of the synergy of energy and water resources, the development path for the coordinated development of energy and water in Jincheng was put forward from different angles, such as the reform of energy structure and the introduction of water-saving technology, so as to provide a scientific basis for the decision-making departments of Jincheng to formulate development plans to achieve the “double carbon” goals.

4.2. Low Emissions Analysis Platform

The low emissions analysis platform (LEAP) was developed by the Stockholm Environment Institute and has been adopted by more than 190 countries and numerous organizations worldwide [38]. It is a scenario-based modeling tool that can be used to track energy consumption, production, and resource extraction by sector. LEAP includes technology and environment database (TED) containing data for hundreds of technologies. TED describes the technical characteristics, costs, and environmental impacts of energy technologies, citing reports from dozens of agencies, including the Intergovernmental Panel on Climate Change (IPCC). The LEAP-Jincheng was constructed to forecast energy and carbon emissions under this study’s different policy and technology choices. The sectors in LEAP-Jincheng include the three industrial sectors, divided into agriculture, industry, construction, transportation, commerce, and others. The section also considers the residential, divided into urban and rural areas. In energy processing and conversion, the LEAP-Jincheng model considers the following sectors: coal mining and transformation, electric power and heat production, and natural gas production.

4.3. Kaya Equation

The Kaya equation was proposed by Kaya in 1989 [39]. Kaya believes that CO₂ emissions are determined by population, living standards, energy intensity, and carbon emission factors [40]. The specific Kaya equation is shown below.

(1)$C=\frac{C}{E}\times \frac{E}{GDP}\times \frac{GDP}{P}\times P$

(2)$CE=\frac{C}{E},EI=\frac{E}{GDP},G=\frac{GDP}{P}$

where C, E, GDP, and P represent total carbon emissions, energy consumption, gross domestic product, and population, respectively. CE, EI, G, and P represent carbon emission factors, energy intensity, economic size, and population size. LEAP-Jincheng model was constructed by Equation (3) according to the Kaya equation.

(3)$C_i=CE\times E{I}_i\times G\times P$

where $C_i$ represents the CO₂ emission from different industrial industries (million tons), i is industrial sectors (i = 1, 2, …, n), CE is the IPCC emission factor preset in the LEAP model.

4.4. Accuracy Verification

The metrics in Table 2 are used to check the performance of the model. Ratio of standard deviation (RSD) is the index used to check the precision of the model simulation results. Relative error (RE) can reflect the credibility of the model. Mean absolute percentage error (MAPE) is a relative measure used to characterize the accuracy of the model’s prediction results.

Among them, ${\widehat{y}}_i$: modelled total annual energy consumption of sector i. $y_i$: total annual energy consumption in sector i. $\overline{y}$: average of total sectoral annual energy consumption [41].

The range of RSD is [0, +∞]. The range of RE is [−∞, +∞]. The range of MAPE is [0, +∞]. Table 3 shows the classification of model performance for these metrics in different numerical ranges.

4.5. Elastic Coefficient Analysis

The elastic coefficient was used to evaluate the synergistic reduction in energy and water resources by various emission reduction measures in different scenarios. The calculation formula for the energy–water synergistic elasticity coefficient is as follows:

(4)$\mathrm{E}=\frac{\mathsf{\Delta }{\rho }_e/{\rho }_e}{\mathsf{\Delta }{\rho }_w/{\rho }_w}$

where $e$ and $w$ indicate energy and water resources; $\mathrm{E}$ represents the synergy degree of energy–water reduction. ${\rho }_e$ and ${\rho }_w$ represent the total energy and water consumption under the BS, respectively. $\mathsf{\Delta }{\rho }_e$ and $\mathsf{\Delta }{\rho }_w$ represent reductions in energy consumption and water use in other scenarios compared to the BS, respectively. Therefore, $\mathsf{\Delta }{\rho }_e/{\rho }_e$ is the change rate of energy consumption reduction. $\mathsf{\Delta }{\rho }_w/{\rho }_w$ is the change rate of water consumption reduction.

If $\mathrm{E}⩽0$, relevant measures in this scenario only reduce energy utilization. If $\mathrm{E}>0$, the scenario has the synergistic reduction effect of energy and water. Further, if $\mathrm{E}=0$, it indicates that the reduction degree of energy and water is the same. If $0<\mathrm{E}<1$, the reduction degree of water consumption is higher than energy consumption. If $\mathrm{E}>1$, the reduction degree of energy consumption is higher than water consumption [42].

5. Scenario Settings

LEAP-Jincheng consists of key assumptions, energy consumption, energy production and conversion, and resources. Taking 2020 as the base year, three scenarios were set in this study. The BS was set based on the forecast trends of the economy and demographics. The PS was set by the constraint of government planning documents. The IS was set with the goal of strong energy conservation and accelerating industrial adjustment. In this study, the departments considered include agriculture, industry, construction, transportation, commerce, other tertiary industries, urban residential, and rural residential. The energy types include electricity, natural gas, gasoline, kerosene, diesel, coal, heat, and coke. In addition, the water consumption index is used to analyze the water consumption of the energy production sector, and global warming potential (GWP) is used to calculate the future carbon emissions of each scenario.

5.1. Baseline Scenario (BS)

The BS is the reference for other scenarios. It is established without interfering with the economic development and natural population growth of Jincheng. It can represent the future development trend of the energy–water in Jincheng. In this scenario, the future forecast values of key assumptions, such as GDP and population from 2021 to 2050, are set based on the relevant forecast values of PwC. The industrial structure and energy intensity of various sectors are consistent with the base year 2020. The import proportion of coal, electricity, heat, and other energy remains unchanged. Energy production is the difference between total consumption and total import. The water consumption rate is maintained. The ratio of circulating cooling to air cooling is assumed to be 1:1. The specific parameters of the BS are shown in Table 4.

5.2. Policy Scenario (PS)

The PS is based on the BS but adds the constraints of policies and planning at all levels of China, Shanxi Province, and Jincheng. This scenario adjusts GDP, population, urbanization rate, energy structure, industrial structure, and power generation structure. This scenario has faster GDP and population growth than the BS. The energy structure adjustment mainly increases natural gas, reduces coal, and increases electricity. The relevant policies and regulations are shown in Table 4, and the main parameter settings are shown in Table 5.

5.3. Intensified Scenario (IS)

The IS is based on the PS. The setting parameters of the energy consumption sector and the production and conversion sector are backward deduced according to the “double carbon” goals to achieve peak carbon emissions by 2030 and a significant reduction in carbon emissions by 2050. Key assumptions, such as population, GDP, and industrial structure, are consistent with PS. Technological progress is reflected in a gradual reduction in energy intensity across sectors. The transformation of the energy consumption structure is reflected in the change in the proportion of all types of energy consumption, further reducing the proportion of coal consumption and increasing the proportion of clean energy and electricity consumption. The main parameter settings are shown in Table 6. Energy intensities for each scenario are provided in Appendix A.

6. Results and Discussions

6.1. The Forecast of Energy, Water, and Carbon Emissions

In order to verify whether the LEAP-Jincheng model is in line with reality, the simulation results of 2020 were compared with the statistics of energy consumption of various sectors in the Jincheng Statistical yearbook. The evaluation sectors are: primary industry, industrial, construction, transport, commercial, other, and residential. Table 7 shows the calculation results of each performance metrics of the LEAP-Jincheng model in 2020.

All metrics show good performance. LEAP-Jincheng model can complete accounting and prediction well. Figure 4 shows the energy consumption of Jincheng from 2020 to 2050. In the BS, with the development of the economy and population, the total energy consumption will reach 38.98 million tons of coal equivalents in 2050, 2.1 times that in 2020. Due to the lack of technological innovation and policy adjustment, the overall energy consumption structure has changed little. Coal is still the energy type with the largest energy consumption, accounting for 62.7% of the total energy consumption. In the PS, with the promotion of national energy-saving policies and the improvement of the power generation structure, the total energy consumption is expected to remain stable after 2030. By 2050, the total energy consumption will be 32.89 million tons of coal equivalents, which is 15.62% lower than that in the BS. The clean transformation of energy structure has reduced the proportion of coal consumption in Jincheng. By 2050, the coal consumption has a total decrease of 18.12%, compared to BS. The proportion of clean energy, such as electricity and natural gas, has increased significantly. In the IS, the proportion of thermal power generation has been reduced, further reducing the proportion of coal consumption. Due to the further adjustment of the power generation structure, even if power consumption increases, coal consumption will only be 8.52 million tons of coal equivalents in 2050. The total energy consumption is 22.57 million tons of coal equivalents, saving 31.38% of energy, compared to the BS. It can be seen that the IS plays a significant role in increasing renewable energy, such as coalbed methane and controlling coal, and has achieved remarkable results in energy conservation and consumption reduction.

Figure 5 shows the total amount of direct and indirect carbon emissions in Jincheng from 2020 to 2050. The three scenarios differ significantly in carbon emissions. In the BS, the total carbon emission continues growth, and the total carbon emission in 2050 will be 102.76 million metric tons, nearly double that in 2020. This shows that according to the current social and economic development trend, the substantial future increase in carbon emissions will significantly impact the climate. In the PS, due to the larger GDP development target and urbanization ratio, carbon emissions in this scenario are higher than those in the BS until 2037. However, carbon emissions have remained stable with a slight decline, indicating that the integrated development of renewable energy and traditional fossil energy has effectively restrained the further increase in carbon emissions. In the PS, the annual carbon emissions in 2050 totaled 80.88 million metric tons, which reduces by 21.3%, compared to the BS. However, the total carbon emissions from 2020 to 2050 were not significantly different from the BS, which had no effective emission reduction effect. In the IS, the carbon emissions reach a peak of 68.77 million metric tons in 2030 and subsequently decreased continuously. The carbon emissions in 2050 will be 50.72 million metric tons, which reduces by 50.64%, compared to the BS. In the IS, the cumulative emissions from 2020 to 2050 were 1871.54 million metric tons, which reduces by 555.32 million metric tons, compared to the BS. The advancement of new energy equipment technology and the utilization of clean energy under the IS scenario lay strong conditions for realizing the “double carbon” goals. Therefore, the IS is suitable to guide the future low-carbon development of Jincheng. Carbon emissions by demand sector under each scenario are shown in Appendix B.

Figure 6 shows the energy production and water consumption in energy production and conversion sectors in Jincheng from 2020 to 2050. In the BS, the large coal production leads to a continuous increase in energy production and conversion in the future, reaching 2.5 million TJ in 2050, 0.45 million TJ more than in the PS, and 1.16 million TJ more than in the IS. The water consumption in three scenarios all increase compared with the base year, among which the PS shows the strongest growth. In 2050, the water consumption in the PS will double compared with 2020, reaching 117.39 million m³. The increase in water consumption is mainly due to the mass production of energy, especially thermal power generation in electricity production. This scenario suggests that advanced cooling technologies will be needed in the future to use less water on a low-carbon basis. Although the IS limits energy production to a certain extent, it is necessary. The decrease in energy consumption due to scientific and technological progress leads to a reduction in energy production and conversion. In the IS, the water consumption in 2050 will be 67.76 million m³, and the accumulated water consumption is 2110.18 million m³ from 2020 to 2050, which is 10% less than the BS. Therefore, the IS is the most water-saving scenario among the three scenarios. The detailed values of energy production for each scenario are provided in Appendix C.

To determine whether there is a synergistic effect between energy and water consumption under PS and IS, the elasticity coefficient of energy–water synergy was calculated for each scenario (Table 8). In the PS, the energy consumption will be reduced by 6.04 million tons in 2050, the water consumption will be increased by 19.72 million m³. The elastic coefficient was −0.77 (E < 0), which indicates that energy–water synergy is not present in PS, and the goal of energy–water coordinated development from the perspective of low-carbon cannot be achieved. In the IS, the energy consumption will be reduced by 16.35 million tons in 2050, and the water consumption will be reduced by 29.91 million m³. The elastic coefficient was 1.37 (E > 0), which indicates that this scenario can realize the coordinated reduction in the emissions of energy and water, which is a suitable path for the future low-carbon development of Jincheng.

6.2. Uncertainties and Limitations

The sensitivity of LEAP-Jincheng model was analyzed. By adjusting the value of the parameter in the model, the influence degree of changing the parameter value on the model behavior was calculated. Sensitivity calculation formula is as follows:

$S_Q=\left|\frac{\mathsf{\Delta }{Q}_{\left(t\right)}}{Q_{\left(t\right)}}\cdot \frac{X_{\left(t\right)}}{\mathsf{\Delta }{X}_{\left(t\right)}}\right|$

Among them: $t$ is time, $Q_{\left(t\right)}$ is the value of state $Q$ at time $t$, $X_{\left(t\right)}$ is the value of the parameter $X$ at time $t$, $S_Q$ is the sensitivity of the state variable $Q$ to the parameter X, $\mathsf{\Delta }{Q}_{\left(t\right)}$ and $\mathsf{\Delta }{X}_{\left(t\right)}$ are the growth of state variable $Q$ and parameter $X$ at time $t$, respectively.

The 11 parameters in the model selected for sensitivity calculation are: GDP (A1), population (A2), urbanization rate (A3), energy intensity of the primary industry (A4), energy intensity of industry (A5), energy intensity of construction (A6), energy intensity of transportation (A7), energy intensity of commerce (A8), energy intensity of others (A9), energy intensity of urban (A10), and energy intensity of rural (A11). The study analyzed how much a 10% change in parameters from 2020 to 2050 would affect 11 parameters to judge the impact of parameters on the whole system. The sensitivity results are shown in Table 9.

It can be seen from the sensitivity calculation results that the sensitivity values of GDP and industrial energy intensity are greater than 0.05. The sensitivity values of other parameters are not more than 0.05. The results show that the system is insensitive to most parameters. The system is stable. GDP and industrial energy intensity are the main uncertain factors, indicating that economy and industry are the main factors affecting the development of Jincheng’s future energy system.

In addition to that, this study still has the following limitations: first of all, the data mainly come from the yearbook and relevant statistics of the Bureau of Statistics, which has the problem of simplifying energy types. Therefore, the difference between various energy emission factors will lead to calculation errors in the carbon emission calculations. Secondly, in scenario development, this study mainly adjusts the energy sector with no specific planning value for water resources. Future energy–water-related research needs to better understand water resource use and consumption and set more detailed water-saving policies to reflect more realistic energy–water development.

6.3. Policy Suggestions

Based on the LEAP-Jincheng model and energy–water synergy analysis, among the three scenarios, the IS can realize the purpose of energy-saving and water-saving at the same time, which is suitable for the future low-carbon development of Jincheng. Therefore, the IS scenario is the best to achieve sustainable development and “double carbon” goals. By comparing the three scenarios in this study, policies and measures for low-carbon development of various departments in Jincheng are proposed.

Promoting clean coal and clean energy development. As a resource-based city dominated by coal, emissions can be reduced through the coordinated development of coal bed methane, coal, and coal power in the future. At the same time, it increase the investment and technology development of new energy, such as renewable hydrogen [47]. The establishment of urban sustainable smart energy systems can be promoted [48]. In these ways, a sample city can be built from technology development, infrastructure construction to integrated energy systems.

Strengthening the green transformation of energy and the transformation of greenhouse gases. The research and development investment in renewable energy technologies should be increased, and the development technologies for the naturally occurring green energy, such as neem gum, should be actively introduced to promote Jincheng’s transformation from a resource-based city dominated by fossil energy to a new green city dominated by renewable energy and technology [49,50]. At the same time, research on catalysts for greenhouse gas conversion should be carried out, and mature technologies, such as the hydrogen production through the dry reforming of methane, should be widely introduced to reduce greenhouse gas emissions in Jincheng city and promote the sustainable development of Jincheng’s environment [51].

Strengthen new energy generation technology. At present, the electric power sector in Jincheng is still based on thermal power generation. Thermal power generation consumes a lot of coal and water resources. In the future, the power sector should further deepen reform, continuously reducing the proportion of thermal power generation, vigorously developing wind and marsh gas power generation, and promoting the integration and application of solar photovoltaic power generation systems.

Carry out industrial restructuring. In the future, Jincheng will continue to rely on the coal industry as the main economic pillar. The excessively high proportion of coal will result in a series of structural constraints on social and economic development, resulting in insufficient economic power development in the future. Therefore, it is suggested to start from the coordinated development of the energy and non-energy industries and gradually make way for energy intensive industries to the orderly development in the new industry.

Raise the national awareness of low carbon. Relevant studies [52] show that energy-saving attitude, subjective norm, and environmental knowledge have an impact on energy-saving behavior of young users. Through the formulation of policies, knowledge training, and other methods to enhance the public awareness of emission reduction, reduce energy waste, and promote consumers’ low-carbon behavior, the emission reduction in the entirety of society can be fundamentally promoted.

Develop water-saving technologies and reduce water consumption in energy production sectors. Significant water savings are possible for industrial, commercial, and residential end uses from efficient technologies and behaviors. In particular, more advanced water-saving technologies should be introduced in the energy production sector. For example, using mixed cooling for air cooling instead of primary cooling.

Develop policies that consider both energy and water development. Change the mutual independence of energy and water resources, consider the water resources in Jincheng while formulating energy policies, and jointly promote low-carbon development.

7. Conclusions

From 2010 to 2020, the total energy consumption and carbon emissions in Jincheng continued to grow, in which industry is the leading energy consumer. The promotion of water-saving technology has reduced industrial water consumption, but the problem of energy efficiency is still prominent at present. In order to explore the path of coordinated development of energy and water resources from a low-carbon perspective in Jincheng, this study established the LEAP-Jincheng model with 2020 as the base year. The results show that compared with the BS, the IS has achieved remarkable results in energy conservation and consumption reduction. In 2050, the total energy consumption is 22.57 million tons of coal equivalents, saving 31.38% of energy, compared to the BS. The BS nearly doubled total carbon emissions by 2050, compared with 2020. Although the PS achieves a carbon emission peak by 2030, the future development momentum is insufficient, and the goal of low-carbon development is not realized. In the IS, carbon emissions reach a peak of 68.77 million metric tons in 2030 and then continue to decrease to 50.72 million metric tons in 2050, which is 50.64% lower than the BS. This scenario effectively achieves the carbon peak target and has positive significance for carbon neutrality. The water consumption for energy production and conversion shows that the water consumption in the BS and PS continues to grow. The water consumption in the IS gradually decreases when it reaches the peak in 2034. The cumulative water consumption of IS in 2020 to 2050 is 2110.18 m³, which is 10% less than that in the BS. The results of energy–water synergy in Jincheng show that the elastic coefficient was 1.37 in the IS, it is the best scenario for Jincheng to achieve coordinated development of energy and water resources from the low-carbon perspective. This study can provide theoretical support for the low-carbon development of energy–water collaboration for resource-based cities and have an important driving significance for China to realize “double carbon” goals. At the same time, it can provide effective technical methods and data support for further research by other scholars in this field.

In addition, the LEAP-Jincheng model still has some limitations in the use of data due to the simplified statistics of energy types in the statistical yearbook and the lack of specific planning for water resources use in the energy sector. Therefore, the work can be further improved in two aspects: First, in terms of data, more reliable data should be collected in a centralized manner. Field surveys should be targeted to specific sectors to obtain more detailed information on energy consumption, water consumption, and carbon emissions. Second, in terms of scenario setting, more renewable energy technologies and water-saving technologies should be introduced to explore the realization of “double carbon” goal in cities.

Author Contributions

Conceptualization, Y.Z. and X.L.; methodology and formal analysis, G.L.; validation, D.J., J.F. and Y.Z.; data curation, Y.Z.; writing—original draft preparation, Y.Z.; writing—review and editing, X.L.; visualization, J.F.; funding acquisition, G.L. and D.J. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the author.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. The location of Jincheng and the main river.

Figure 2. (A) Energy consumption by Sector from 2010 to 2020. (B) Energy production from 2010 to 2020. (C) Water use for agriculture, industry, and household from 2010 to 2020.

Figure 3. The research framework.

Figure 4. Energy consumption by scenarios from 2020 to 2050.

Figure 5. Future carbon emission trend of the three scenarios.

Figure 6. Trends of energy production and water consumption in energy production and conversion sectors under three scenarios.

Table 1

Parameters of water consumption for coal and natural gas production, electricity generation, and heating.

Department	Unit	Water Consumption
Coal mining	m3/t	0.27
Coal washing	m3/t	0.07
Coking	m3/t	1.8
Gas extraction	m3/103 m3	2
Heating	m3/GJ	0.018
Thermal power generation (cyclic cooling)	m3/WMh	2.6
Thermal power generation (air cooling)	m3/WMh	0.7
Thermal power generation (once-through cooled)	m3/WMh	0.54
Biomass power generation	m3/WMh	0.19
Wind power generation	m3/WMh	0.03

Table 2

Performance metrics.

Metrics	Expression	Range	Remarks
Ratio of Standard Deviation	$RSD=\frac{\sqrt{\frac{1}{N}{{\displaystyle {\sum }_{i=1}^N\left({\widehat{y}}_i-y_i\right)}}^2}}{\sqrt{\frac{1}{N}}{\displaystyle {\sum }_{i=1}^N{\left(y_i-\overline{y}\right)}^2}}$	$0\le RSD\le \infty$	The metric approaching zero shows better performance
Relative Error	$RE=\frac{{\widehat{y}}_i-y_i}{y_i{}_i}100\%$	$-\infty \le RE\le \infty$
Mean Absolute Percentage Error	$MAPE=\frac{100\%}{n}{\displaystyle \sum _{i=1}^n\left|\frac{{\widehat{y}}_i-y_i}{y_i}\right|}$	$0\le MAPE\le \infty$

Table 3

Performance criteria of performance metrics.

Performance	RSD	RE	MAPE
Very good	[0.0, 0.5]	[−100%, 100%]	The metric approaching zero shows better performance
Good	(0.5, 0.6]
Adequate	(0.6, 0.7]
Inadequate	(0.6, +∞)	(−∞, −100%)∪(100%, +∞)

Table 4

Key assumption settings for the baseline scenario.

	2020	2025	2035	2045
GDP growth rate	4.312%	3.058%	2.389%	1.059%
Population growth rate	0.172%	−0.466%	−0.239%	−0.386%

Table 5

Sources and contents of policy scenario.

Policy	Level	Policy Contents	Scenario Settings
Research Report on Building a Modern Energy System to Promote the Transformation and Development of High Quality and High-Speed Energy [43]	Jincheng	Compared with 2020, the GDP will double by 2030 and triple by 2050.	Set a linear increase in GDP between 2020–2030 and 2030–2050.
		By 2025, the urbanization level will rise to over 65%, and by 2030, the urbanization level will reach 70%, in line with the national level.	The urbanization rate is set to increase linear between 2020–2030 and 2030–2050.
		Increase coalbed methane and renewable energy, control coal, and promote the integrated development of renewable energy and traditional fossil energy.	By 2050, the share of coal will be reduced by 20%, and it will be supplemented by natural gas and electricity.
Outline of the 14th Five-Year Plan for National Economic and Social Development of Jincheng Urban Area and The Vision Goal of 2035 [44]		Accelerate upgrading new energy equipment technology, focusing on wind power equipment, photovoltaic equipment, and electric vehicle equipment.	By 2050, the proportion of thermal power generation reduced by 50%, biomass, wind, photovoltaic, hydroelectric power increased by 5%, 2%, 13%, and 30%, respectively.
		Accelerate the development of electrification with a focus on transportation.	By 2050, the share of gasoline vehicles will be reduced by 30%, the share of electric vehicles and new energy vehicles will be increased by 20% and 10%.
		Increase the use of renewable energy for heating.	Reduce coal heating in the thermal sector by 20% by 2050, supplemented by solar energy.
Proposal of the CPC Shanxi Provincial Committee on Formulating the 14th Five-Year Plan for National Economic and Social Development and the Long-range Goals of 2035 [45]	Shanxi Province	By 2025, the economic aggregate of the province will increase significantly, the leading role of industry in economic development will be significantly enhanced, and the proportion of industrial added value in GDP will increase rapidly.	To increase the share of secondary industry by 3% by 2025
		Accelerate changes in the structure and mode of energy use and promote green ways of production and living.	By 2050, the share of residential electricity and natural gas will increase by 5% each, oil consumption will decrease by 10%.
		Green, intelligent, and safe coal mining and efficient, clean, and deep coal utilization to lead the country.	Improve energy intensity. See Appendix B for energy intensity settings of each sector.
		Continue to focus on improving the quality of water ecological environment and coordinate the conservation and utilization of water resources.	/
Modern Energy System Planning in the 14th Five-year Plan [46]	China	Energy, green, and low-carbon transformation project.	See Appendix B for energy intensity settings of each sector.
		Remarkable results have been achieved in saving energy and reducing consumption.

Table 6

Main parameter settings in each scenario.

Main Parameter	BS				PS				IS
	2020	2030	2040	2050	2020	2030	2040	2050	2020	2030	2040	2050
GDP (RMB/billion)	142.6	202.2	262.9	308.4	142.6	285.1	356.4	427.7	-	-	-	-
Population (Thousand)	2108.3	2148.1	2078.3	2011.3	-	-	-	-	-	-	-	-
Urbanization rate (%)	62.7	63	-	-	62.7	70	73	74	-	-	-	-
The industrial structure (%)	4:54:42				-	2:54:44	1:45:54	1:4:59	-	-	-	-
Changes in industrial energy intensity (%)	-	-	-	-	−1.7	−0.5	-	-	−3	−1.6	-	-
Changes in household energy intensity (%)	-	-	-	-	−1.5	-0.8	−0.2	-	−1.5	−1.2	−1	-
Proportion of thermal power generation (%)	96.1	-	-	-	-	80:6	77.5	75	-	70	60	50

Table 7

Performance metrics of the LEAP-Jincheng during calibration and validation.

Performance Metrics		Performance Results
RSD	0.03	Good performance
RE	0.01	Good performance
MAPE	0.22	Good performance

Table 8

The synergy of energy–water reductions in each scenario by 2050.

	$\mathbf{E}⩽0$	$\mathbf{E}>0$
		$0<\mathbf{E}<1$	$\mathbf{E}=1$	$\mathbf{E}>1$
	No Synergy	Synergy,Water Saving More	Synergy, Same Degree	Synergy,Energy Saving More
PS	−0.77	/	/	/
IS	/	/	/	1.37

Table 9

Sensitivity of each parameter.

	A1	A2	A3	A4	A5	A6	A7	A8	A9	A10	A11
$S_Q$	0.9787	0.0213	0.0049	0.0054	0.9430	0.0022	0.0159	0.0159	0.0053	0.0152	0.0061

Appendix A

Table A1

Changes in energy intensity of each sector (J/Chinese yuan).

	2020		2030		2040		2050
	PS	IS	PS	IS	PS	IS	PS	IS
Primary industry	501.41	501.41	426.28	377.38	395.75	343.02	387.91	326.25
Industrial sector	6948.98	6948.98	6139.52	5198.30	5839.36	4423.97	5553.87	3764.99
Construct sector	248.53	248.53	220.47	185.92	209.69	158.22	199.44	134.65
Transport sector	959.81	959.81	848.01	686.07	806.55	589.84	767.12	507.10
Commercial sector	393.70	393.70	347.84	340.19	330.83	307.66	314.66	278.25
Service sector	65.55	65.55	65.55	53.84	55.09	46.52	52.39	42.07

Appendix B

Table A2

Predicted carbon emissions for each scenario in demand sectors (Million MT CO₂ Equivalent).

	2020			2030			2040			2050
	BS	PS	IS	BS	PS	IS	BS	PS	IS	BS	PS	IS
Primary industry	0.20	0.20	0.20	0.28	0.16	0.14	0.37	0.09	0.08	0.43	0.10	0.09
Industrial sector	38.51	38.51	38.51	54.62	64.88	54.44	71.00	60.41	44.82	83.30	57.37	37.57
Construct sector	0.04	0.04	0.04	0.06	0.08	0.07	0.08	0.08	0.06	0.10	0.08	0.05
Transport sector	0.49	0.49	0.49	0.70	0.82	0.62	0.91	1.08	0.68	1.07	1.20	0.61
Commercial sector	0.19	0.19	0.19	0.27	0.35	0.30	0.35	0.51	0.36	0.41	0.63	0.36
Service sector	0.04	0.04	0.04	0.06	0.07	0.07	0.07	0.11	0.09	0.09	0.14	0.11
Urban sector	0.58	0.58	0.58	0.57	0.49	0.49	0.55	0.45	0.43	0.53	0.44	0.37
Rural sector	0.44	0.44	0.44	0.43	0.30	0.28	0.41	0.23	0.20	0.40	0.21	0.17
Total	40.50	40.50	40.50	56.99	67.14	56.41	73.74	62.95	46.72	86.32	60.16	39.31

Appendix C

Table A3

Total energy production in each scenario (million TJ).

	2020	2022	2024	2026	2028	2030	2032	2034	2036	2038	2040	2042	2044	2046	2048	2050
BS	1.23	1.33	1.44	1.52	1.6	1.69	1.79	1.89	1.97	2.06	2.15	2.25	2.35	2.4	2.45	2.5
PS	1.23	1.43	1.61	1.78	1.9	2.02	2.03	2.05	2.04	2.03	2.01	2.03	2.05	2.06	2.06	2.05
IS	1.23	1.39	1.53	1.61	1.65	1.69	1.66	1.63	1.6	1.55	1.5	1.48	1.45	1.42	1.39	1.35

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