A System Dynamic Analysis of Urban Development Paths under Carbon Peaking and Carbon Neutrality Targets: A Case Study of Shanghai

Abstract

In 2021, under the carbon peaking and carbon neutrality targets of China, Shanghai declared that it would peak carbon emissions by 2025. This would require the formulation of specific and effective implementation paths of low-carbon development. This paper conducts a dynamic study on Shanghai’s carbon dioxide emissions by establishing a system dynamic model of Shanghai’s economy-energy-carbon emission. It studies the implementation path of Shanghai’s carbon peaking and carbon neutrality targets by scenario analysis. The results show that under the Baseline Scenario, Shanghai’s carbon emissions will peak by 2035, which is later than what the government promised. However, the Carbon-Peak and Deep-Low-Carbon Scenarios suggest that Shanghai can achieve the carbon peaking target in 2025, and the CO₂ emission intensity will drop by 89.2% and 92.4%, respectively, by 2060. Improving the industrial energy utilization efficiency and the proportion of clean electricity is crucial for Shanghai to reduce carbon emissions. The transportation sector will become the main sector of urban energy consumption in the late stage of carbon neutralization. Without considering carbon sinks, the costs of achieving carbon neutrality for the three scenarios are approximately 5.68 billion, 2.79 billion and 1.96 billion USD, respectively. Finally, this paper puts forward relevant suggestions on promoting the transformation of energy structure, and strengthening specific emission reduction measures of various departments, to provide references for Shanghai’s policy formulation.

1. Introduction

Human activities have resulted in massive emissions of greenhouse gases and caused serious climate problems. In April 2016, many countries around the world formally signed the Paris Agreement, which clarified the long-term goal of keeping the global average temperature rise below 2 °C and striving to limit the temperature rise to below 1.5 °C [1]. As the world’s largest carbon emitter, China has set a goal to peak carbon emissions by 2030 and strive to achieve carbon neutrality by 2060 (hereafter referred to as “dual carbon” target) [2], and incorporates the above goals into the country’s overall development strategy.

As the main gathering place of industrial and economic activities, cities are responsible for 80% of global greenhouse gas emissions [3]. The continuous expansion of the scale of urbanization has resulted in the continuous growth in human demand for energy and resources, which brings socio-economic development and improves residents’ living standards but also negatively impacts the climate and environment. This shows that urbanization is the main driving force of carbon emissions [4,5]. China’s urbanization process is developing rapidly. In 2020, China’s urbanization rate was 63.89%, exceeding the world average of 56.15%. With the advance of China’s urbanization process in the future, cities will remain the main source of carbon dioxide emissions. Therefore, strengthening carbon emission reduction in cities is crucial for China to achieve its carbon peaking and carbon neutrality targets.

As the economic center of China, Shanghai’s GDP in 2020 was 3870.058 billion Yuan, seven times what it was in 2000, accounting for 3.8% of the China’s total GDP. Rapid economic growth has led to an increase in energy demand in Shanghai. As shown in Figure 1, in 2020, Shanghai’s total energy consumption was 110.996 million tons of standard coal, which is 2.1 times the total energy consumption in 2000 and shows a continuous growth trend. The energy intensity of Shanghai decreased from 1.09 tons of standard coal/10,000 Yuan in 2000 to 0.29 tons of standard coal/10,000 Yuan in 2020. Although Shanghai is experiencing a continuous decline in energy intensity due to the optimization of its industrial structure and the improvement of energy utilization efficiency, its carbon dioxide emissions still reached 193 Mt of CO₂ in 2019, which is higher than other municipalities directly under the central government, surpassing Yunnan, Gansu and other provinces [6]. In 2021, Shanghai actively responded to the national call and took the lead in putting forward the goal of reaching carbon peaking in 2025. However, there are certain challenges confronting Shanghai’s goal of reducing carbon emissions: they include large energy demand, tight emission reduction time, and unclear emission reduction technology routes. Therefore, Shanghai must develop a more specific and feasible implementation path to achieve its carbon peaking and carbon neutrality targets. What is the focus of low-carbon transformation? How to specifically promote the low-carbon transformation of the city? How to implement the “dual carbon” target in different industries? These issues are of great significance to the exploration and formulation of practical transformation plans.

From the perspective of carbon emissions, the paper discusses the dynamic correlation between urban development and low-carbon transformation, studies the dynamic change path of the “dual carbon” target of an urban system, and considers the comprehensive impact of different variables on individual factors, strengthening the overall and partial collaborative thinking. However, currently there is little research on the transformation path of Shanghai to achieve the “dual carbon” target. Therefore, the paper broadens thinking about the research direction and implementation path of urban low-carbon development.

In addition, this paper makes a more detailed decomposition and transmission path setting for the main industrial sectors, considering the development trend, transformation mode and transmission path of different industries in a concrete, multi-dimensional and in-depth way. In addition, this paper considers the impact of energy trading in the energy market on local carbon emission and transformation. The establishment of an electricity carbon emission accounting mechanism is of certain practical significance for local low-carbon transformation and strengthening regional carbon emission cooperation in the future. At the same time, it can also provide a reference for the city and other areas to achieve the carbon peaking and carbon neutrality targets, and low-carbon, green and sustainable economic development.

With Shanghai as a case study, this paper uses the system dynamic model to study the implementation path of the “dual carbon” target at the city level from the perspectives of industry, technology and policy. By establishing a system dynamic model, the paper analyzes the dynamic relationship between urban carbon emissions and urban economic development, industrial structure and energy structure, and uses scenario analysis methods to explore different paths to achieve the “dual carbon” target. The structure of this paper is as follows:

Section 2 reviews current research on low-carbon transformation and the implementation path of the “dual carbon” target and puts forward the innovation points of this paper. Section 3 discusses the establishment of the system dynamic model of Shanghai’s economy-energy-carbon emission. The rationality and validity of the model will also be tested. Section 4 sets the Baseline, Carbon-Peak and Deep-Low-Carbon Scenarios in combination with the targets of achieving carbon peaking and carbon neutrality in Shanghai, and conducts three different emission reduction analyses. Then, the main results of each scenario are comprehensively analyzed and discussed through the model. Section 5 summarizes the main conclusions of this study and puts forward relevant suggestions and measures.

2. Literature Review

As economic growth will consume more energy [7], the conflict between energy needs and environmental needs has become increasingly prominent. Whether for energy or environmental needs, low-carbon transformation has become a common goal pursued by the world in recent years. In particular, major emission economies should accelerate the low-carbon transformation [8]. However, low-carbon transformation is a complex and dynamic social project. It covers a wide range of fields, so scholars have different views on the research perspective and methods of low-carbon transformation.

Some scholars are committed to the analysis and research of low-carbon transformation at the macro level. Zhang et al. [9], Li et al. [10] and Yang et al. [11] establish an evaluation index system to verify the effect and driving factors of low-carbon transformation in the region. Alajmi [12] uses an LMDI model to analyze the carbon emissions of Saudi Arabia through the activity effect, energy effect and population effect. These methods can effectively quantify the impact of carbon emission drivers in a region, and find the direction and focus of emission reduction for the regional low-carbon transformation. However, the limitation of the above methods is that they can only be used to analyze and test the changes of historical data, and cannot be used to predict the dynamic changes of future carbon emission related factors [13].

Therefore, many authors take the industry as the research object, and combine economic development [14], energy intensity [14], energy structure [14], industrial structure [15] and other factors affecting carbon dioxide emissions to study the transformation development and path of the steel industry [14,16], power industry [15,17,18], construction industry [19] and transportation industry [20]. Because of the high carbon emissions of the above industries, it is meaningful to carry out research on them. However, different industries are interrelated. In addition, the government should consider the transformation of all industries in the region from a more comprehensive and detailed perspective to achieve the “dual carbon” target.

Hong et al. dynamically simulated the path of China’s carbon peak and the trend of global climate change from 2020 to 2050 by building the RICE-LEAP model, and concluded that China would achieve the carbon peak goal as early as 2029 [21]. Cai et al. used the LEAP model to predict the way to achieve carbon peak and carbon neutralization in Bengbu, China [22]. Carpos et al. used the PRIMES model to evaluate the decarbonization decarburization of the EU in 2030 under the “Clean Energy for all Europeans” package [23]. Wu et al. estimated the future trend of carbon dioxide emissions in 18 developed countries by using the extended STIRPAT model and combining 6 carbon emission drivers [24]. Huang et al. used the extended STIRPAT model and the LEAP-Beijing model to evaluate the carbon emission reduction priorities and paths of Beijing under different scenarios from 2015 to 2060 [25]. However, the LEAP model mainly depends on the judgment of experts [26] and multiple linear problems are easily generated between different factors of STIRPAT model [26]. The system dynamic method has certain advantages in the implementation path of carbon neutrality. This method can cover the entire economic system, and can more systematically and comprehensively analyze the impact, changing trends, and policy scenarios of different industries and factors to formulate a more feasible path for the implementation of the “dual carbon” target.

Through analysis of the existing literature, we found some deficiencies: (1) There is insufficient granularity in the scenario analysis of the “dual carbon” target. Although industry is the largest energy consumption sector [27], few articles have subdivided it in modeling and scenario analysis, and there are differences in the amount of energy consumption and carbon emission reduction potential of different industries in the industrial sector, so subdividing the industrial sector will help cities to implement differentiated emission reduction goals and reduce emission reduction costs. In addition, studies on the prediction of “dual carbon” targets at the city level rarely consider the indirect carbon emissions of urban electricity and the impact of energy trading in the energy market on local carbon emissions reduction and the cleanliness of the power structure. (2) Most articles focus on considering the unilateral impact of economy, energy and industry on carbon emissions, but in urban systems, there will also be interactions or even multi-directional interactions between carbon emission drivers.

Therefore, the innovation of this paper lies in: Firstly, this paper discusses the transformation path of key industries and sectors in Shanghai under the constraint of the “dual carbon” target. Secondly, the industry sectors are refined in the process of modeling and scenario analysis. In the modeling and analysis stage, industries are split and decomposed, and different industries are divided into six energy-intensive industries and emerging industries. The development paths and emission reduction potential of different industries are discussed. For the construction sector, the dynamic changes of energy consumption and carbon emission reduction during the construction and operation phases of buildings are considered in detail. Finally, considering the impact of energy trading in the energy market on local carbon emissions and transformation, a carbon emissions accounting mechanism for electricity is established, focusing on the dynamic changes of carbon emissions from electricity trading.

3. Mechanism and Methods

The system dynamics method has been widely used in studies of carbon dioxide emissions. System Dynamics (SD) was established by Forrester [28]. Huo et al. combined a building energy terminal model with an SD approach, and used scenario analysis and Monte Carlo simulation to explore the peak and peak time of emissions in the building industry [29]. Wen et al. used an SD model to simulate the carbon emission system of China’s freight transport and explore the carbon peaking path of the freight system in 2030 [30]. Gisele et al. used a system dynamic model combined with an energy matrix to evaluate the energy consumption and CO₂ emissions of road transport in Brazil [31]. Proaño et al. used a system dynamic approach to assess the techno-economics of CO₂ capture and utilization in the cement industry [32]. In this section, an SD model of Shanghai’s economy-energy-carbon emission is established through the transmission framework of the urban economic system and energy consumption.

3.1. Simplify Framework

Figure 2 shows the idea of building the model in this paper. The model shows the carbon emission and transformation path of the whole socio-economic system from the demand-side perspective through the dynamic mechanism. First of all, in the city’s economy-energy-carbon emission system, the energy consumption and carbon dioxide emissions of different industries are mainly affected by the internal drive of demand. Economic development requires more energy consumption, while the improvement of technologies reduces energy demand. Energy consumption is highly related to carbon emissions, while the transformation of energy structure contributes to carbon emission reduction.

However, the proposal of the “dual carbon” target will profoundly guide the transformation of urban economy and industry, and promote the overall green transformation of cities. Socio-economic development level, energy efficiency level, energy structure, industrial structure, policy planning and other factors are the main factors affecting carbon dioxide emissions [7,13,33]. Under the rigid constraints of the “dual carbon” target, the level of economic development, energy efficiency industrial structure and energy structure will be improved and optimized. Economic development will bring about the growth of carbon emissions [7], while the optimization and improvement of energy efficiency, energy structure and industrial structure will significantly reduce carbon emissions [13]. Policy planning will curb carbon emissions through indirect effects [34]. Since carbon emissions mainly come from energy consumption [35], the driving role of the above factors on carbon emissions will also affect energy demand. According to the existing research summary, it is found that the impact of the above factors on energy demand is consistent with the impact on carbon emissions [36,37,38]. Therefore, the constant change and interaction of energy efficiency, energy structure, industrial structure and other factors will externally promote the change of the scale of terminal energy demands and different types of energy demand, thus affecting the change of urban carbon dioxide emissions. Therefore, the terminal energy demand of different industries is calculated by combining the above internal and external factors and the transmission path of different industries.

Then, the total urban demand for different types of energy is determined according to the summary of different sectors’ demand for different types of energy. Finally, as carbon sinks can effectively absorb carbon dioxide [25], carbon dioxide emissions are calculated according to energy consumption, CO₂ emission coefficients of different varieties and carbon sinks.

3.2. Methods

The carbon emission calculation method and construction process of the model in this paper are as follows:

3.2.1. Carbon Emission Calculation Method

In this paper, CO₂ emissions are calculated using the method provided by the United Nations Intergovernmental Panel on Climate Change (IPCC) [39]. The formula is as follows:

$$C={{\displaystyle \sum }}_i^n\left(E_i\times E{F}_i\right)$$

Among them, C represents the CO₂ emission of energy consumption, $E_i$ represents the physical quantity of the i-th energy consumption, $E{F}_i$ represents the CO₂ emission factor of the i-th energy source, and i represents coal, oil, natural gas and electricity respectively.

3.2.2. System Dynamic Model

This paper uses the Anylogic software to build a simplified simulation model of Shanghai’s economy-energy-carbon emission system based on the framework of the urban economy carbon emission system in Section 3.1. The Anylogic software is widely used in the establishment of discrete events, SD, and multi-agent models for research in business, passenger logistics, supply chain and social public security etc. [13,40,41,42]

The model is shown in Figure 3. In the SD module of the Anylogic software, the elements are mainly divided into stock, flow, auxiliary variable and parameter. Stock represents the accumulation of elements in the system; Flow represents the change of inventory over time; Auxiliary variables are usually used to express the relationship between elements; Parameters are constants. Moreover, the causal chain is a representation of a causal relationship between correlated variables, and the table function is a dataset. Shanghai’s economy-energy-carbon emission system is composed of six subsystems: primary industry, industry, construction, transportation, tertiary industry and power. The specific modeling methods and construction process are as follows:

(1)

Primary industry subsystem

Primary industry accounts for a small proportion of Shanghai’s industrial structure. The historical data of the past decade shows that the GDP of Shanghai’s primary industry accounts for less than 1% of Shanghai’s GDP, and there is a downward trend. Considering the integrity of the economic system, we choose to simplify the consideration of the primary industry. We determine the terminal energy consumption of the primary industry and the demand for different types of energy through the added value of the primary industry and the energy consumption per unit of added value.

(2)

Industry subsystem

China’s secondary industry is divided into industry and construction. Industry, as the main source of carbon emissions in China [10,27,43], is the key to future carbon emission reduction. Due to the differences in factors such as energy consumption, energy intensity, technology level, and impact on CO₂ emissions in different industries, further detailed research is required. According to the 2011 to 2019 data, China’s high-energy-consuming industries account for more than 75% of industrial energy consumption, which means that the high-energy-consuming industries account for most of the industrial CO₂ emissions. This paper divides the industries into high energy-consuming industries and other industries. According to the “2010 National Economic and Social Development Statistical Report”, the high-energy-consuming industries are divided into six industries: petroleum processing, coking and nuclear fuel processing, chemical raw materials and chemical products manufacturing, non-metallic mineral products, ferrous metal smelting and rolling processing, non-ferrous metal smelting and rolling processing, and electricity and heat production and supply. Therefore, in the industry subsystem, paths are set for the above-mentioned six industries and other industries. As the growth of industrial added value often leads to an increase in the demand for energy, the level of energy consumption per unit of added value in the industry will also affect the terminal energy consumption of the industry.

(3)

Construction subsystem

Based on data from the International Energy Agency, in 2019, the CO₂ emissions generated by the operation of buildings in China were about 2.1 billion tons, accounting for about 20% of the country’s total CO₂ emissions. The “Shanghai ‘Twelfth Five-Year’ Building Special Energy Conservation Plan” divides the total energy consumption in the construction sector into the building industry energy consumption and the building operation energy consumption. The energy consumption of the construction industry refers to the production and construction energy consumption of construction enterprises; and the energy consumption of building operation refers to the energy consumption of terminal equipment for maintaining the building environment and the energy consumption of terminal equipment for various activities in buildings [44]. In the construction subsystem, we consider building construction energy consumption as the energy consumption of the construction industry.

Since building operating energy consumption is embodied in many industries such as industry, transportation, and residential consumption, and due to the limited availability of data, this paper adopts the method captured in the “China Building Energy Consumption Research Report 2016” [44] in dealing with building operating energy consumption. For the conversion method of building energy consumption, we only consider the residential energy consumption, and regard the embodied energy consumption of other buildings as the energy consumption of other industries.

Since the total urban building area is a key factor affecting the energy consumption and CO₂ emissions in the building sector, the core variable of the construction subsystem is the total urban building area. With the development of urbanization and population growth, the demand for various types of buildings in Shanghai has gradually increased. Following the above definition, the annual construction energy consumption of the urban construction area is the energy consumption of the construction industry. According to statistics, half of the total existing urban construction area in Shanghai is the area of residential buildings, and the various types of terminal energy consumption generated by residential buildings can be regarded as the energy consumption of residential consumption.

(4)

Transportation subsystem

Due to its special geographical location, Shanghai has a great demand for energy in the transportation field. In 2019, Shanghai’s aviation transportation consumed 11.26 million tons of standard coal, followed by water transportation, which consumed 10.01 million tons of standard coal. Road transportation consumed 4.86 million tons of standard coal, and railway transportation consumed 570,000 tons of standard coal. The aviation sector has become the main area of energy consumption in the transportation sector in Shanghai, primarily because Shanghai, as a national and global aviation hub, carries out a large number of air transport tasks. However, due to the cross-regional nature of aviation and water transportation, it is difficult to calculate energy consumption; thus, this paper mainly considers the CO₂ emissions of the urban road transportation industry. Aviation transportation, water transportation, railway transportation and other transportation are considered as a whole. We divide urban road traffic into private cars, taxis and buses. The calculation formulas for the above three types of transportation energy consumption are derived from the report “Net-Zero Emissions from Urban Transportation” [45]. Vehicle energy consumption is affected by factors such as the number of vehicles, energy efficiency and mileage driven. With the economic development and the improvement of residents’ living standards, the demand for private cars continues to grow, and the consumption of oil products is also increasing. Therefore, vigorously developing green public transportation and promoting use of new energy vehicles are feasible carbon emission reduction measures. Due to the limited data, this paper divides the types of private cars, taxis, and buses into gasoline vehicles and electric vehicles.

(5)

Tertiary industry subsystem

Shanghai has a high proportion of tertiary industry and a high level of development in the service industry. According to the Shanghai Energy Balance Sheet, the tertiary industry is divided into the transportation, warehousing and postal industry, wholesale, retail and accommodation, catering, and others. As the transportation industry is considered separately, in the tertiary industry subsystem, other sectors apart from the transportation sector are considered. The increase in the GDP of the tertiary industry mainly depends on the economic development of other tertiary industries except the transportation sector. In view of relevant plans, the direction of Shanghai’s future development will continue to be dominated by the tertiary industry; thus, the energy demand of the tertiary industry sector will be under great pressure.

(6)

Power subsystem

As the largest source of carbon emissions in China [15], the power sector is one of the focuses of this paper. Nowadays, the economic and social operation of a city is largely dependent on the operation of the power system. Power energy is closely related to coal, and power plants in Shanghai are mainly coal-fired ones. Considering the power generation structure of the city, we distinguish between thermal power generation and clean energy power generation. Thermal power refers to coal-fired power generation and gas-fired power generation, and clean energy refers to wind power, hydropower and solar power. Clean energy power generation in Shanghai is mainly solar energy and wind energy, but the proportion is low. According to existing data, wind power generation in Shanghai was 1.893 billion kWh in 2020, accounting for 2.2% of Shanghai’s power generation. Although clean energy such as wind energy does not produce carbon emissions, it is very relevant and necessary to consider clean energy construction and clean energy substitution factors when studying the urban energy transition path and carbon emission reduction.

Shanghai’s limited geographical location and resource endowment lead to the city’s power supply structure including energy trading and local production. According to the data of the past decade, Shanghai’s dependence on the purchase of electricity from other provinces through the energy market has gradually increased, accounting for 51% in 2019, and there is a trend of further increase. The main target of Shanghai’s power and energy trading is hydropower from Sichuan and other southwest regions. Furthermore, in the power generation structure, as the proportion of oil-fired power generation remains around 1% all year round, this paper only considers coal-fired power generation and gas-fired power generation when considering the city’s thermal power generation structure. Coal, natural gas and other energy consumed by thermal power generation in Shanghai have been calculated based on their respective energy consumption levels. This paper establishes an accounting mechanism for electricity trading and CO₂ emissions. The CO₂ emissions of electricity in this paper include the indirect CO₂ emissions of purchased electricity. Since it is difficult to determine the power generation method of the purchased electricity, the cleanliness of Shanghai’s purchased electricity is estimated based on the proportion of China’s thermal power generation to the total generation.

Moreover, to ensure the integrity of the model and the rationality of the results, the coking coal consumption, heating coal consumption, and power loss are simplified and considered in this paper.

3.3. Data Sources

In this model, 2011 is used as the base period, 2011–2020 is the model historical period, 2021–2060 is the model planning period, and the time step is one year.

As required by the model, the CO₂ emissions of Shanghai from 2011 to 2020 were calculated. The GDP, building area, number of vehicles and power generation of each industry come from the “Shanghai Statistical Yearbook” over the years. The terminal energy consumption of each industry and the energy consumption of different varieties come from the “China Energy Statistical Yearbook” over the years. We divided energy into four types: coal, oil, natural gas and electricity. Among them, the standard coal conversion factor and carbon dioxide emission factor of different energy sources are shown in

Table 1. Standard coal conversion factor and carbon dioxide emission factor for different types of energy.

Type of Energy	Standard Coal Conversion Factor	CO2 Emission Factor	Data Source
Coal	0.80715 kgce/kg	1.9003 kgco2/kg	Shanghai Industrial Energy Efficiency guidelines (2008), Carbon Trade Web of China
Oil	1.457125 kgce/kg	3.0459 kgco2/kg	Shanghai Industrial Energy Efficiency guidelines (2008), Carbon Trade Web of China
Natural gas	1.33 kgce/m3	2.1622 kgco2/m3	Carbon Trade Web of China
Electricity	0.1229 kgce/kWh	0.42 kgco2/kWh	Shanghai Industrial Energy Efficiency guidelines (2011), Shanghai Municipal Bureau of Ecology and Environment

. Part of the relevant forecast indicator data comes from the plans, reports and papers of relevant departments such as Shanghai’s “14th Five-Year Plan”, “China 2050: A Zero-Carbon Vision for a Fully Modernized Country”, and “Net-Zero Emissions from Urban Transportation” [45,46].

3.4. Model Verification

After the system dynamic model is built, it is necessary to verify the reliability and rationality of the model by historical data. We imputed the data of odd-numbered years from 2011 to 2020 into the model, and then compared the model simulation results of 2011 to 2020 with the historical data and used the relative error index as the evaluation basis. The results are shown in

Table 2. Relative errors of the main variables of the model.

Year	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
AVPI	0%	2%	2%	−5%	−4%	14%	14%	10%	10%	−5%
AVHEI	1%	8%	11%	8%	8%	8%	10%	3%	2%	14%
AVOSI	0%	9%	9%	7%	7%	6%	6%	−4%	−4%	−5%
AVTI	0%	1%	2%	5%	6%	3%	4%	−7%	−6%	−2%
AVOTI	1%	2%	5%	6%	3%	4%	−7%	−7%	−3%	−3%
CD	22%	2%	−2%	9%	7%	2%	−2%	−2%	−2%
OD	−6%	2%	3%	10%	6%	0%	1%	13%	1%
ED	−7%	−1%	1%	8%	6%	0%	0%	0%	−1%
NGD	−9%	−6%	−3%	6%	2%	−2%	−5%	−5%	−5%
TEC	−6%	−6%	−5%	4%	5%	1%	−3%	−4%	−2%	1%
EG	−6%	−4%	−7%	11%	6%	3%	−3%	−3%	−2%	−5%
PITEC	6%	5%	2%	−7%	−4%	−14%	−14%	−15%	−10%	−8%
HEITEC	7%	9%	12%	14%	9%	2%	12%	21%	8%
OSITEC	0%	7%	9%	14%	7%	4%	6%	6%	−4%
CITEC	−3%	−2%	−4%	−2%	−11%	−10%	−2%	0%	−6%	−10%
TITEC	1%	0%	0%	2%	6%	6%	5%	3%	0%	21%
OTITEC	−6%	−2%	2%	0%	6%	6%	4%	−3%	−7%	1%
RCTEC	−2%	−1%	−3%	−5%	−1%	−2%	1%	1%	6%	8%

.

For most of the studies that adopt system dynamic analysis, as long as the relative errors of most variables are between −15% and +15%, the model is considered to be reliable. In this paper, the verification results in the table are actually the optimal results after rounds of adjustment. In the process of determining the relationship between different variables and the transmission path, we constantly modify and adjust the variables in the model to minimize the error between simulation results and historical data and improve the reliability of the model. The possible reason for the large error in 2020 is that the real data declined due to the impact of the COVID-19 in 2020. We found that the error of terminal energy consumption in the high-energy-consuming industries in 2018 is large and positive. This aspect may be because at the end of 2017, China proposed a new development concept of innovation, coordination, greenness, openness, and sharing. The high-energy-consuming industries have reduced the energy consumption of the industry by increasing investment in technology, research, and technological innovation. However, since 2018, the United States has imposed tariffs on Chinese products, which has affected the import and export of some energy-intensive industries, resulting in a decline in output, which in turn affects the energy consumption of the industry. Moreover, the time delay in the transmission process of economic operations and energy consumption in the initial stage of model operation, resulted in relatively large errors in some variables such as coal demand in the early stage of simulation. In Table 2, some variables in 2020 are vacant because official statistics have not yet been released. Overall, in the simulation results of the model, more than 98% of the relative errors are in the range of −15% to 15%, and about 90% of the relative errors are in the range of −10% to 10%, which proves that the model is effective and reliable results will be produced.

4. Results and Analysis

4.1. Analysis of Carbon Emissions

According to the model results, we now analyze the change process and development trend of carbon dioxide emissions, carbon dioxide emission intensity and energy consumption structure in Shanghai from 2011 to 2020.

Figure 4 shows Shanghai’s total CO₂ emissions, CO₂ emission intensity and CO₂ emissions from coal, oil, natural gas and electricity from 2011 to 2020. From the perspective of the city’s total carbon dioxide emissions, the period of 2011 to 2020 generally showed a downward trend.

Regarding CO₂ emission intensity, from 2011 to 2020, the CO₂ emission intensity of Shanghai decreased each year. In Figure 4, we can see that the city’s total carbon dioxide emissions showed a stable and fluctuating state from 2011 to 2020, and there was a small gap between the peak and the valley but CO₂ emission intensity has maintained a downward trend since 2011, which may be affected by economic development.

Coal and oil are the main sources of CO₂ emissions in Shanghai. In 2019, coal and oil CO₂ emissions accounted for about 81% of the total carbon dioxide emissions. However, Shanghai’s coal CO₂ emissions show a downward trend. Oil CO₂ emissions maintained an emission trend of rising first and then falling. Oil CO₂ emissions were 107.5 million tons in 2020. Natural gas CO₂ emissions have generally maintained an upward trend, increasing from 9.866 million tons in 2011 to 19.69 million tons in 2020, an increase of 99.6%. Electricity CO₂ emissions are showing a steady upward trend. In 2011, the CO₂ emission of electricity was 14.08 million tons, and by 2020, it was 25.03 million tons.

4.2. Scenario Analysis

Section 4.1 clearly presents the CO₂ emissions of different energy sources and the energy consumption structure in Shanghai, which lays a realistic foundation for us to carry out the scenario analysis of Shanghai’s “dual carbon” target path.

Following the carbon peaking and carbon neutrality targets, to analyze the emission reduction effects of different factors, we set up three scenarios: the Baseline Scenario, the Carbon-Peak Scenario, and the Deep-Low-Carbon Scenario. The Baseline Scenario is a scenario in which appropriate emission reduction measures are taken according to the historical development trend. The Carbon-Peak Scenario is an emission reduction scenario that takes carbon peaking in 2025 as the priority target. The Deep-Low-Carbon Scenario is an emission reduction scenario that further strengthens the variables related to industry and power structure on the basis of the Carbon-Peak Scenario.

As China’s economy has entered the stage of high-quality development, the economy has shifted from high-speed growth to medium-high speed growth, which has led to the decline of GDP growth rate in the future [13,47]. In the Baseline Scenario, based on relevant reports [47] and actual conditions, we set the GDP growth rate of various sectors in the future, and the specific data are shown in

Table 3. The GDP growth rate of various sectors.

	2021–2025	2026–2040	2041–2060
GDP (%)	5	4	3
PI (%)	1	0.5	0.5
NMPI (%)	5	1	−1.5
FMSRPI (%)	3	1	−1.5
NMSRPI (%)	1	1	−1.5
EHPSI (%)	3	1	−1.5
CRMCPM (%)	5	1	−1.5
PPCNFPI (%)	3	1	−1.5
OSI (%)	4	3	0.5
TI (%)	5.5	4.5	3.5

. On the basis of ensuring economic development, we appropriately control the technological progress factors of various sectors and adjust the energy structure. The electrification level of the construction sector (i.e., the construction industry sector) and the residential sector will reach 75% by 2060 [46], the electrification level of the primary industry in 2060 will be 100%, and the electrification level of the tertiary industry except the transportation sector is also projected to be at 100%. The development of energy replacement technology in the transportation sector is slow, so it is difficult to achieve complete electrification. In the model, the proportion of electric vehicles in the transportation sector is set to reach 60% in 2060. Because steel, chemical, and other industries rely on coal and oil as raw material inputs in the industry, it is impossible to achieve zero consumption of coal and oil. Therefore, the model assumes that after 2026 the consumption of industrial coal and oil will decrease by 2% and 1.5% each year, respectively, while the electrification level will increase to 60% by 2060. According to the World and China Energy Outlook 2060 [47], China’s natural gas consumption will peak in 2040. Compared with other provinces, Shanghai has the advantages of capital, innovative technology, and policy, so the natural gas consumption is set to peak five years ahead of schedule.

Under the Carbon-Peak Scenario, we aim to peak emissions by 2025, and set higher requirements for the technical level of each department after 2025. The first is to set the unit coal consumption level of thermal power generation in the power structure. It will maintain a growth rate of 0.5% from 2021 to 2025, an average annual growth rate of 1% between 2026 and 2040, and a growth rate of 1.2% from 2041 to 2060. The second is the energy consumption of the industrial sector. After 2025, the energy utilization rate of various industries will increase at an average annual rate of 2%. We also focus on reducing oil consumption by aviation and water transportation in the transportation sector. According to historical data, oil consumption is the main source of CO₂ emissions in the transportation sector, but research from the International Civil Aviation Organization (ICAO) shows that after the global aviation industry has adopted various technical and management measures, fuel efficiency can only improve by 1.39% per year [45]. Therefore, in this scenario, the oil and natural gas energy consumption levels of aviation, water transportation, and other sectors are set to increase by 15% by 2040. Moreover, due to the continuous improvement of the cleanliness of external power in the power structure and the application of the “West-to-East Power Transmission” project and related transformation technology, we increase the proportion of external power purchased. According to the goal of vigorously developing the city’s offshore wind power, solar energy, hydrogen energy, and other renewable energy in the “14th Five-Year Plan for Ecological Environment Protection in Shanghai” and other related plans, we correspondingly increase the proportion of the city’s renewable energy generation in the model and reduce carbon dioxide emissions from the power sector through multiple approaches. In the energy structure, the peak time for natural gas is placed at 2030.

The Deep-Low-Carbon Scenario is a more positive presupposition for some variables on the basis of the Carbon-Peak Scenario. According to the relevant reports, the thermal power industry, petroleum, and chemical industry have the greatest energy saving potential; thus, in this scenario, the energy utilization rate of thermal power, petroleum, and chemical industry are further improved [48]. Second, there is also an improvement of the oil utilization efficiency of the transportation sector by 15% in 2040 and another 10% in 2060. Finally, we decrease the unit coal consumption of thermal power generation and increase the proportion of renewable energy power generation in the city.

As the main sector of energy consumption, industry plays a key role in improving energy efficiency in various industries and reducing energy consumption in cities. This is one of the main points considered in this paper. As shown in

Table 4. The average annual rate of technological progress in major industrial sectors.

	Standard Scenario			Carbon-Peak Scenario			Deep-Low-Carbon Scenario
	2021–2025	2026–2040	2041–2060	2021–2025	2026–2040	2041–2060	2021–2025	2026–2040	2041–2060
NMPI (%)	1	1.5	1.5	1	2	2	1	2	2
FMSRPI (%)	1	1.5	1.5	1	2	2	1	2	2
NMSRPI (%)	1	1.5	1.5	1	2	2	1	2	2
EHPSI (%)	1	1.5	1.5	1	2	2	1	2.5	2.5
CRMCPM (%)	1	1.5	1.5	1	2	2	1	2.5	2.5
PPCNFPI (%)	1	1.5	1.5	1	2	2	1	2.5	2.5
OSI (%)	1	1.5	1.5	1	2	2	1	2	2

, we set the average annual rate of technological progress in major industrial sectors under three scenarios.

This paper also considers the urban power structure. With the socio-economic development and the advancement of “electrification”, the demand for electricity will continue to grow. The power structure involves coal utilization, clean energy substitution, etc., and is a key factor affecting urban carbon dioxide emissions. In the future, Shanghai will increase its efforts to purchase clean power such as southwest hydropower, and increase the proportion of purchased clean power. Meanwhile, in the city’s power generation structure, the scale of offshore wind power and photovoltaic power generation has gradually expanded, and the proportion of renewable energy power generation in Shanghai will be greatly increased in the future. However, in the short term, limited by resource endowment and investment scale, the development of renewable energy in Shanghai is relatively slow, and the local power structure is still dominated by thermal power generation. Therefore, reducing the coal consumption of thermal power generation and withdrawing from outdated power generation installations are necessary measures to reduce the energy consumption of high carbon emissions such as coal in this city. The main variables in the above initiatives are shown in

Table 5. Setting of variables related to power structure.

	Standard Scenario			Carbon-Peak Scenario			Deep-Low-Carbon Scenario
	2025	2040	2060	2025	2040	2060	2025	2040	2060
Proportion of purchasing clean electricity from other provinces (%)				40	100	100	40	100	100
Proportion of electricity generation in the city (%)			40			30			30
Proportion of renewable energy (%)	8		50	8	30	60	8	40	80
The progress rate of standard coal consumption per unit of thermal power generation (%)	0.5	0.8	0.8	0.5	1	1.2	0.5	1.2	1.2
Proportion of coal in power generation (%)			60			45			45

Note: Blank area indicates variable changes with historical trend.

.

4.3. Results

4.3.1. CO₂ Emissions

Section 4.2 presented more detailed parameter settings for the industrial sector and power structure. The urban CO₂ emissions under the three scenarios are shown in Figure 5. Generally, the CO₂ emissions in the three scenarios from 2021 to 2060 show a trend of first increasing and then decreasing. Specifically, from the point of view of the peak time, the carbon peaking time under the Baseline Scenario will be 2035, while the Carbon-Peak Scenario and the Deep-Low-Carbon Scenario both will project a peak in carbon dioxide emissions in 2025. In the Baseline Scenario, the peak of carbon dioxide emissions is 266.7 million tons, indicating that with the continuous economic development, the entire city will generate more CO₂ emissions, but through the adjustment of the energy structure, Shanghai’s carbon dioxide emissions will show an inverted U shape. Although the Baseline Scenario can achieve carbon peaking in 2035, it is far from Shanghai’s carbon peaking target. Therefore, it is necessary to conduct more active and effective measures regarding the main factors affecting CO₂ emissions in the entire economic system. In the Carbon-Peak Scenario, the CO₂ emissions will peak in 2025 at 240.9 million tons, while in the Deep-Low-Carbon Scenario, carbon dioxide emissions will peak in 2025 at 239.9 million tons, showing a decrease of 1 million tons compared to the Carbon-Peak Scenario. In 2060, the CO₂ emissions under the Carbon-Peak Scenario and the Deep-Low-Carbon scenario will be 85.26 million tons and 59.85 million tons, respectively, which will be 64.6% and 75% lower than those under the two scenarios in 2025. However, by 2050, the EU’s domestic greenhouse gas emissions will be reduced by at least 80% [23]. Compared with EU countries, there is still a certain gap in the planned reduction of carbon dioxide emissions in Shanghai in the future. The difference in CO₂ emissions shows that strengthening the proportion of renewable energy in the power structure, energy efficiency, transportation fuel consumption and other related variables can play a more critical role in reducing emissions. Regarding CO₂ emission intensity, it can be seen from

Table 6. Result of carbon peaking and carbon neutrality under different scenarios.

	Peak Time (Year)	Peak Carbon Emissions (104 t)	Carbon Emissions in 2060 (104 t)	Carbon Intensity in 2025 (t/104 Yuan)	Carbon Intensity in 2060 (t/104 Yuan)
Baseline Scenario	2035	26,670	17,360	0.5	0.107
Carbon-Peak Scenario	2025	24,090	8526	0.493	0.053
Deep-Low-Carbon Scenario	2025	23,990	5985	0.49	0.037

that the baseline scenario is much higher than the other two scenarios. In 2060, the CO₂ emission intensity of the Baseline Scenario will be projected to be about 0.107 ton/10,000 Yuan, which is 78.6% lower than that in 2025. In the Carbon-Peak Scenario, it will decrease by 89.2%, and in the Deep-Low-Carbon Scenario, it will decrease by 92.4%. This is very close to China’s carbon emission intensity, which will drop by 92.4% by 2050 [13]. The difference in CO₂ emission intensity between the Carbon-Peak Scenario and the Deep-Low-Carbon Scenario is relatively small.

Regarding energy consumption, the different scenarios present an inverted U shape. The peaks of energy consumption in the Baseline, Carbon-Peak and Deep-Low-Carbon Scenarios will appear in 2041, 2034, and 2030 respectively, later than their respective peak time of carbon emissions. Some scholars have found similar research results [13]. Due to the necessity of social development, the social demand for electricity is also increasing. With the application and promotion of electrification and the development, purchase, and replacement of renewable energy, clean electricity will be more desirable than fossil energy such as coal and oil, and will lead to a decrease in the proportion of fossil energy consumption such as coal in the energy structure. The adjustment of the energy structure depends on the improvement of the electrification level and the utilization of renewable energy. This is a long-term process, making the peak time of energy consumption later than the peak time of carbon emissions.

4.3.2. Decomposition Analysis of Energy Carbon Emission

We decomposed CO₂ emissions under the Carbon-Peak and Deep-Low-Carbon Scenarios, and the results are shown in Figure 6. Under the Carbon-Peak Scenario, the difference in carbon dioxide emissions between 2025 and 2040 is 60.43 million tons, of which coal, oil, and electricity play a positive role in reducing emissions, while natural gas plays a negative role in reducing emissions. Among them, electricity has the highest contribution to CO₂ emission reduction, with a contribution rate of 47%. Natural gas has a negative contribution because its energy consumption will increase after 2025 and will peak in 2030 due to slow application and promotion of electrification. This may result in a high degree of social dependence on natural gas. From 2040 to 2060, electricity will have achieved zero emissions, and emissions from coal, oil, and natural gas will be greatly reduced, with contributions of about 38.9%, 38.5%, and 22.6% respectively. In the Deep-Low-Carbon Scenario, we found that from 2041 to 2060, oil has the largest CO₂ emission reduction, with a contribution rate of 45.6%. The largest reduction in oil CO₂ emissions between 2040 and 2060 is mainly due to the aviation, water, and other transportation sectors, which will have higher technical standards and emission reduction targets. Under these two scenarios, oil and coal will both fall sharply, and the same will happen in Britain by 2050 [49].

4.3.3. Sectors Energy Consumption Analysis

To further understand the future energy consumption of different sectors, the energy consumption of different sectors is presented in Figure 7. In the Baseline Scenario, the energy demand of the transportation sector, other tertiary industry sector and residential sector all show an upward trend. The industry sector and processing and conversion sectors show a downward trend, and the largest decline is in the industry sector. Compared with 2025, the decline in 2060 will be about 55.7%. Both the primary industry sector and the construction industry sector will have maintained a low percentage of energy consumption. The Carbon-Peak Scenario shows a downward trend except for the transportation sector, while the key sectors for energy consumption reduction are the industry sector and processing and conversion sector. In 2060, the energy consumption of the two sectors only will account for 33.8% of the total energy consumption of the city, while the transportation sector will account for 31.4%. The growth of energy consumption in the transportation sector is mainly due to the increase in trade and the improvement of living standards brought about by the continuous development of the social economy. Aviation and water transportation are frequently used and there is rapid growth in the number of private cars. This will lead to oil being used almost exclusively in the transportation sector, which is similar to Capros’ s and Wu’s conclusion [23,50]. In the Deep-Low-Carbon Scenario, the energy consumption of the transportation sector is reduced by increasing the unit turnover of energy consumption. In the three scenarios, we found that the industry sector has the highest rate of energy consumption in the vast majority of years. This shows that it is difficult for the industrial sector to reduce emissions and the transformation will take a long time.

4.3.4. Analysis of Energy Consumption in Industrial Sector

To further find the key points of emission reduction and understand the energy consumption of the internal structure of the industry, we decomposed the energy consumption of various industries within the industry sector. In considering the total industrial energy consumption, we included the energy consumption of the high-energy-consuming industrial sector, other industrial sector, and processing and transformation processes. We found that under the Baseline, Carbon-Peak, and Deep-Low-Carbon Scenarios, the proportion of total industrial energy consumption in the city’s total energy consumption will be 35.6%, 33.8% and 28.4% in 2060, respectively. Based on the data, although the total industrial energy consumption shows a downward trend, even in the middle of this century, the consumption of industrial energy is still very high. Under the Carbon-Peak and Deep-Low-Carbon Scenarios, the total industrial energy consumption will reach its peak in 2025 and will maintain a decreasing trend in the future. Between 2025 and 2040, there is a slow decline in the total industrial energy consumption. The pull of power rates is weaker, and electrification and renewables are on a smaller scale. The period between 2041 and 2060 is the deep low-carbon stage. With the continuous progress of industrial technology, energy intensity will continue to decrease. In the Carbon-Peak and Deep-Low-Carbon Scenarios, the industrial energy intensity in 2060 will decrease by 78% and 82%, respectively, compared with 2025. Lower energy intensity means lower energy consumption [13], so the total industrial energy consumption will decline at a faster rate from 2041 to 2060.

The industrial sector has the greatest potential for reducing emissions [51]. We further decomposed the terminal energy consumption of major industrial sectors and the results are shown in Figure 8. We find that in the Carbon-Peak Scenario, the energy consumption of ferrous metal smelting and the rolling processing industry will decrease the most, and there will be a difference of 13.27 million tons of standard coal between 2025 and 2060. Under the guidance of relevant policies such as the “Carbon Peaking Action Plan before 2030”, with the continuous deepening of supply-side reform, backward production capacity will be withdrawn, the industrial structure of the steel industry will be continuously optimized, and the industry will actively promote green and low-carbon transformation. The application and promotion of technologies such as iron-making technology and the all-scrap electric furnace industry will contribute to the rapid decline of energy consumption in the industry. However, it is difficult for the steel industry to realize carbon neutrality [52]. The energy consumption of chemical raw material and the chemical product manufacturing, the petroleum processing, coking and nuclear fuel processing industry, and the non-metallic mineral product industry all will decline significantly. Other secondary industry will experience an increase in energy consumption from 2011 to 2040, and the energy consumption in 2040 will be 10.95 million tons of standard coal. This is mainly due to Shanghai’s efforts to develop strategic emerging industries such as integrated circuits, biomedicine, artificial intelligence, and other emerging industries, and the rapid development and scale expansion of the industry. It will result in an increase in energy consumption, but after 2040, with the breakthrough of emission reduction technology in related fields and the reduction of the growth rate, energy consumption will decline. Compared with the Carbon-Peak Scenario, the energy consumption trend of major industries in the industrial sector in the Deep-Low-Carbon Scenario is similar, but the energy consumption of the electricity and heat production and supply industry, the petroleum processing, coking and nuclear fuel processing industry, and the chemical raw material and chemical products manufacturing industry is lower. This shows that the effect of reducing energy consumption of various industrial sectors is more obvious under the Deep-Low-Carbon Scenario.

4.3.5. Power Structure Analysis

Power structure is another factor considered in this paper. The promotion of electrification will lead to a rising demand for electricity in society [53]. If electricity is not clean and green, it will increase the pressure on carbon emission reduction. Therefore, the cleanliness of the power structure will greatly affect the future carbon dioxide emissions of Shanghai and the realization of the “dual carbon” target. Thermal power generation is the absolute main force of carbon dioxide emissions at present, and the city can better change the status quo by using clean energy as a replacement and clean energy trading.

Regarding electricity CO₂ emissions, in order to better study and distinguish the power structure and electricity CO₂ emission effects, we add the CO₂ emissions of urban thermal power generation in the CO₂ emissions of electricity. As shown in Figure 9, under the Baseline Scenario, due to the slow purchase of clean electricity from other provinces, the CO₂ emissions of electricity will always remain high. In 2060, the CO₂ emissions of electricity would be 78.74 million tons, accounting for about 45.4% of the total CO₂ emissions. However, under the Carbon-Peak and Deep-Low-Carbon Scenarios, the proportion of electricity CO₂ emissions in the total CO₂ emissions in 2060 will be about 15% and 11.9%, which shows a significant drop. This is because the two futuristic scenarios increase the proportion of clean electricity purchased in Shanghai. Since thermal power is the main source of carbon emissions affecting the city, we can quickly increase the proportion of clean electricity purchased from other provinces through energy market transactions. On the one hand, the city can actively consume hydropower from the southwest and help the southwest region solve the problem of production and sales. On the other hand, increasing the proportion of clean energy in Shanghai and relieving the pressure of thermal power generation in the city can effectively reduce the city’s CO₂ emissions. This shows that the Carbon-Peak and Deep-Low-Carbon Scenarios are effective in decarbonization of power structure. As the proportion of clean electricity purchased from other provinces increases, the CO₂ emissions in all scenarios will decrease significantly. This shows that the cleanliness of electricity from other provinces affects the CO₂ emission reduction in Shanghai [25]. Under the Carbon-Peak and Deep-Low-Carbon Scenarios, we find a turning point in 2040. This is because we assume that Shanghai’s externally purchased electricity will be 100% clean after 2040 and the CO₂ emissions of electricity after 2040 will only come from its generation of thermal power.

Therefore, to further understand the city’s power structure, we decomposed and compared the changes as shown in Figure 10. Under the Baseline Scenario, in 2060, the city’s renewable energy will account for only 50% of the city’s electricity generation. The EU countries can achieve this target by 2035 and aim to surpass 65% in 2050 [23]. Iceland’s share of renewable energy power generation even reached 100% in 2018 [54]. Japan will reach 62% in 2050 [55]. The proportion of clean electricity will account for about 48.2% of the total electricity supply. This shows that the development of clean energy in Shanghai is very insufficient. Under the Carbon-Peak Scenario, the proportion of clean electricity purchased from other provinces in 2040 and 2060 will increase rapidly, accounting for 61.2% and 69.4% of the total electricity supply respectively. From 2040 to 2060, renewable energy power generation will increase from 42.33 billion kWh to 77.39 billion kWh. Under the Deep-Low-Carbon Scenario, the proportion of clean electricity purchased from other provinces in the total power supply in 2040 and 2060 is basically the same as that in the Carbon-Peak Scenario, but the proportion of renewable energy power generation in the total power supply in the city will be 15.5% and 24.5% respectively, which shows that during this period, clean energy power would gradually become the main part of the city’s power structure. Rogelj et al. have found that global carbon neutrality needs to be achieved around 2050 under the 1.5 °C scenario. At this time, the proportion of low-carbon electricity will be 97% [56]. In the Carbon-Peak Scenario and Deep-Low-Carbon Scenario, the proportion of non-fossil electricity in Shanghai will be 91% and 94% respectively, which are basically comparable. This shows that in the Deep-Low-Carbon Scenario, the city will invest more in renewable energy, improve the city’s renewable energy power generation capacity, and maximize the level of zero carbonization of electricity by increasing the city’s renewable energy power generation. In the Carbon-Peak and Deep-Low-Carbon Scenarios, the city’s electricity generation will account for 30% of the total electricity supply in 2060. This is because ensuring a certain proportion of the city’s power generation can reduce the risk of instability that may exist in the process of external power transmission, and ensure the stability of the city’s electricity consumption. In the Deep-Low-Carbon Scenario, the city’s total electricity supply in 2040 and 2060 will less than the Carbon-Peak Scenario. This is mainly due to the more efficient energy utilization of various sectors, which reduces unit energy consumption, thus reducing the demand for electricity.

4.4. Sensitivity Analysis

To further understand the impact of some important parameters on CO₂ emissions, we selected variables such as the industrial energy utilization progress rate, the proportion of purchasing clean electricity from other provinces, and the proportion of renewable energy in the city for sensitivity analysis. Sensitivity analysis is a common data analysis method used to quantitatively describe the importance of model input variables to output variables. Sensitivity analysis is currently widely used in different fields, including carbon emissions, ecological environment, technology fields, etc. [57,58].

In the Baseline Scenario, we conducted sensitivity analysis on the values of the above three variables, and the results are shown in Figure 11. In the figure, the variable of industrial energy utilization progress rate varies by 0.5% each time. We found that as industrial energy utilization continued to increase, carbon dioxide emissions would fall sharply. For example, compared with a 2% technological progress rate, a 2.5% technological progress rate would reduce CO₂ emissions by about 10.7 million tons in 2060, a decrease of about 6.6%. By analyzing the proportion of clean electricity purchased from other provinces, we found a gradual decrease in the influence of the proportion of purchasing clean electricity from other provinces on Shanghai’s carbon dioxide emissions. When this variable increased by 10%, carbon dioxide emissions would decrease by 7% to 9%. Finally, we found that the impact of the city’s renewable energy power generation on Shanghai’s carbon dioxide emissions is relatively weak. When the proportion of renewable energy in the city increases by 10%, carbon dioxide emissions correspondingly will reduce by 5% to 7%.

4.5. Carbon Neutrality Cost Analysis

Considering the current level of technology and the emission reduction potential, it is difficult for Shanghai to achieve zero carbon dioxide emissions. Therefore, to achieve net-zero emissions, it is necessary to rely on mechanisms such as carbon sinks and carbon capture. The carbon pricing mechanism will be an important means to achieve the goal of carbon neutrality [59]. Without considering local carbon sinks, this paper estimates the cost of Shanghai’s 2060 carbon neutrality goal using carbon emissions trading. Firstly, we consider the price of carbon. Drawing on the related report [60], we set the carbon price in 2060 at USD327/t. In the three scenarios, Shanghai needs to purchase 173.6 million tons, 85.26 million tons and 59.98 million tons of CO₂ emission trading rights respectively, to achieve carbon neutrality, and the costs of these three scenarios are about 5.68 billion, 2.79 billion and 1.96 billion USD, respectively. However, due to the differences in the Chinese regions, economies, and development directions, there are differences in each province’s CO₂ emission intensity and emission reduction potential; thus, the carbon neutrality target is often difficult for a city or a province to achieve alone, and the carbon neutrality process is more dependent on worldwide implementation.

5. Conclusions

This paper uses the SD method to build a dynamic model of Shanghai’s economy-energy-carbon emission system, calculates the dynamic changes of CO₂ emissions and energy consumption in Shanghai from 2021–2060 from a systematic and comprehensive perspective, analyzes the change of correlation ratio between economic, energy and CO₂ emissions, and simulates the path of Shanghai to achieve the “dual carbon” target under different scenarios. The following conclusions were reached:

(1)

Without accelerating the low-carbon transformation, Shanghai will reach the peak in 2035, and cannot achieve the goal of reaching the peak by 2025. When accelerating the low-carbon transformation of Shanghai, compared with 2025, the CO₂ emission intensity in 2060 under the Carbon-Peak Scenario and Deep-Low-Carbon Scenario will decrease by 89.2% and 92.4%, respectively.

(2)

In the early stage of carbon neutralization, the efficiency and cleanliness of power generation should be promoted. In the later stage, attention should be paid to the improvement of oil emission reduction technology. In the Carbon-Peak and Deep-Low-Carbon Scenarios, electricity will contribute the most to CO₂ emission reduction from 2025 to 2040. From 2040 to 2060, coal and oil will have the highest contribution to emission reduction in the Carbon-Peak Scenario and the Deep-Low-Carbon Scenario, accounting for about 39% and 46%, respectively.

(3)

The transportation sector should strength the emission reduction work in the late stage of carbon neutralization. Under the Carbon-Peak Scenario and Deep-Low-Carbon Scenario, from 2050 to 2060, the energy consumption of this sector will exceed that of the industrial sector and become the industry with the highest energy consumption.

(4)

The improvement of industrial energy utilization efficiency and the purchase and development of renewable energy play a key role in promoting the emission reduction in Shanghai, bringing a huge potential for CO₂ emission reduction to Shanghai. When the progress rate of industrial energy utilization increases from 2% to 2.5%, the carbon dioxide emissions can be reduced by about 6.6%. When the proportion of purchasing clean electricity from other provinces increases by 10%, carbon dioxide emissions will be reduced by 7% to 9%. When the proportion of renewable energy in the city increases by 10%, the carbon dioxide emissions will be reduced by 5~7%.

(5)

The Deep-Low-Carbon Scenario has the lowest cost of carbon neutrality. In the three scenarios, the cost of achieving carbon neutrality in Shanghai in 2060 is about 5.68 billion, 2.79 billion and 1.96 billion USD, respectively.

Based on the above conclusions, we put forward relevant suggestions and measures for the future development direction of Shanghai and the implementation of the “dual carbon” target.

Due to geographical limitations, the level and scale of carbon sequestration in Shanghai are limited. Therefore, the low-carbon transformation of cities mainly depends on the transformation and upgrading of industry, energy and technology.

The reduction potential of CO₂ emission from electricity should be brought into play in a variety of ways and the focus of power development should be dynamically adjusted over time. Due to the long cycle of clean energy construction, Shanghai needs to take the lead in strengthening the development and cooperation with hydropower and other renewable energies in the southwest region, establish a new mechanism for power coordination and trading in the regional energy market, strengthen the construction of transmission related infrastructure, and increase the proportion of clean power in power purchase. On the other hand, Shanghai still needs to improve urban clean energy investment and development. In addition, its decommissioning strategy [61] can be formulated to gradually withdraw backward thermal power plants and reduce standard coal consumption of urban thermal power generation by upgrading power generation plants.

The transportation sector needs to improve the level of energy utilization of aviation and water transportation. Due to the particularity of traffic location and the carbon emission reduction potential from oil consumption, it is necessary to strengthen regional and international cooperation in research and development to improve the cleanliness of oil and special fuels. For CO₂ emission reduction, it will be important to strengthen the construction of the Internet of Things by using big data and other methods, improve the turnover rate of unit commodities, reduce the energy consumption per unit turnover, and improve the railway transportation ratio.

The industrial sector should focus on the emission reduction tasks of high-energy-consuming industries in Shanghai, especially the EHPSI, FMSRPI and CRMCPM. We suggest that government needs to set higher emission reduction targets for industries with greater emission reduction potential, such as CRMCPM and make good use of Shanghai’s capital, enterprise and university resources to increase research and innovation breakthroughs in key emission reduction technologies such as CCS and hydrogen used in steelmaking processes [62], so as to improve the effectiveness of industrial sector technology emission reduction. The city can gradually promote the high-end and intensive development of the city’s energy-intensive industries by strengthening the coordinated development of the Yangtze River Delta region and appropriate industry transfer.

The government should promote improvements in the scale of building integrated power generation and building energy efficiency. Buildings have significant cost and emission reduction potential [19,63,64] and economic performance [65] in the operation phase, and the building density in Shanghai is high. Therefore, it is necessary to improve the electrification level of the building operation cycle, promote the decarbonization of buildings through roof photovoltaic power generation and other integrated power generation methods, and promote the use of green materials to bring more emission reduction to the construction field.

As low-carbon transformation depends on a large amount of capital investment, we suggest that Shanghai should play its role as an economic center, develop sustainable finance [66], establish a sound carbon emission trading market and actively guide the green transformation and upgrading of relevant new energy industries and other industries by taking advantage of capital.