Carbon Emission Analysis of Low-Carbon Technology Coupled with a Regional Integrated Energy System Considering Carbon-Peaking Targets

Abstract

Analyzing the carbon emission behavior of a regional integrated energy system (RIES) is crucial for aligning with carbon-peaking development strategies and ensuring compliance with carbon-peaking implementation pathways. This study focuses on a building cluster area in Shanghai, China, aiming to provide a comprehensive analysis from both macro and micro perspectives. From a macro viewpoint, an extended STIRPAT model, incorporating the environmental Kuznets curve, is proposed to predict the carbon-peaking trajectory in Shanghai. This approach yields carbon-peaking implementation pathways for three scenarios: rapid development, stable development, and green development, spanning the period of 2020–2040. At a micro scale, three distinct RIES system configurations—fossil, hybrid, and clean—are formulated based on the renewable energy penetration level. Utilizing a multi-objective optimization model, this study explores the carbon emission behavior of a RIES while adhering to carbon-peaking constraints. Four scenarios of carbon emission reduction policies are implemented, leveraging green certificates and carbon-trading mechanisms. Performance indicators, including carbon emissions, carbon intensity, and marginal emission reduction cost, are employed to scrutinize the carbon emission behavior of the cross-regional integrated energy system within the confines of carbon peaking.

1. Introduction

China faces a challenging dilemma characterized by the need for rapid economic development, which inherently leads to increased energy consumption. However, the combustion of fossil fuels exacerbates the greenhouse effect, presenting a conflict between economic growth and the imperative for energy conservation and emission reduction. Achieving a harmonious and sustainable balance between the economy and the environment is an urgent issue for the Chinese government and industrial sectors. In response to this challenge, China introduced the “dual carbon” strategy in 2020. This strategic initiative sets forth ambitious goals, aiming to peak carbon dioxide emissions by 2030, also known as “carbon peaking”, and achieve carbon neutrality by 2060, termed “carbon neutrality” [1]. These targets, although commendable, pose significant challenges to socioeconomic development. To meet these goals, innovative energy organizational paradigms have emerged, driven by the promotion of renewable energy applications and other low-carbon technologies [2,3,4]. A notable solution is the regional integrated energy system (RIES), which efficiently combines renewable and fossil energy sources [5], also known as an integrated energy system [6].

In contrast to a single-category energy supply, the RIES represents a multi-energy supply framework that integrates renewable and fossil energy sources. This framework not only enhances the flexibility and reliability of the energy supply [7] but also, more importantly, effectively reduces greenhouse gas emissions by incorporating low-carbon technologies, such as renewable energy. The RIES, thus, provides robust support for achieving carbon reduction strategy goals. Currently, the RIES is experiencing robust development in China, gaining widespread attention from both industry and academia. It has emerged as a crucial energy supply model for realizing the objectives outlined in the “dual carbon” strategy [8].

However, it is essential to highlight that with the introduction of the “dual carbon” strategic goals, the construction plan of the energy system inevitably sets forth constrained targets for carbon emission reduction. The successful achievement of the carbon-peaking target, particularly the more-recent goal, holds paramount importance for carbon neutrality and the realization of long-term emission reduction targets. In this context, the construction planning and optimized operation of the regional integrated energy system (RIES) face new challenges under the current circumstances. Analyzing the carbon emission behavior of the RIES from the perspective of the carbon-peaking development strategy is crucial to plan the construction of the RIES while adhering to the requirements of the carbon-peaking implementation pathway. This analysis is of utmost importance for the successful attainment of the expected goals of carbon peaking. Specifically, this study reveals Shanghai’s RIES carbon emissions under carbon-peaking targets, offering insights for energy system development. Using an extended STIRPAT model, Shanghai could peak emissions by 2025–2035, depending on development paths. CP-RIES, powered by renewables, can autonomously meet targets, while FP-RIES, reliant on fossil fuels, struggles, especially in high-load modes. Market-based policies, like carbon trading and green certificate trading, optimize the load allocation, reducing emissions. Carbon trading is more effective but costly. The key factors include trading prices and renewable quotas, with carbon price having the most significant impact. Although focused on Shanghai, these findings support broader low-carbon transitions, showcasing the impacts of diverse technologies and policies.

The main contributions of this paper can be summarized as follows:

1. The introduction of factors such as the population size, per capita GDP, urbanization rate, industrial structure, energy structure, and energy intensity, considering the existence of the environmental Kuznets curve. The paper extends the STIRPAT model and sets three scenarios, namely, rapid, steady, and green developments, based on China’s and Shanghai’s development plans and relevant policies. The paper predicts the carbon-peaking implementation pathways for Shanghai from 2020 to 2040 in these three scenarios;

2. Based on the penetration level of the renewable energy, three typical RIES system configurations are constructed, including fossil, hybrid, and clean. The integration of energy storage devices is introduced to facilitate the absorption of wind and solar energies, addressing the issue of unstable power output from renewable energy sources;

3. Using the predicted carbon-peaking implementation pathways for Shanghai from 2020 to 2040 as a carbon emission constraint, a multi-objective optimization model is established, including system costs, carbon emissions, and primary energy consumption. This model is used to analyze the carbon emission behavior of the three typical RIES system configurations. In different development scenarios within the carbon-peaking implementation pathways, the paper explores the carbon emission behaviors of RIES systems under the constraint of carbon peaking. It also analyzes the impacts of the carbon-peaking implementation pathway constraint, system configuration, and operational mode on the carbon emissions of individual RIES systems. Additionally, an analysis of marginal abatement costs is conducted;

4. To investigate the role of market mechanisms in promoting carbon emission reduction in the RIES, four carbon emission reduction policy scenarios are established based on carbon-trading and green-certificate-trading mechanisms: a no-policy-impact scenario, a scenario with the sole influence of the green-certificate-trading mechanism, a scenario with the sole influence of the carbon-trading mechanism, and a scenario with the combined influence of the green-certificate- and carbon-trading mechanisms. The multi-objective optimization analysis considers the system costs of the RIES and carbon penalty costs to examine the cross-RIES carbon emission behavior, including carbon emission, carbon intensity, and marginal abatement costs. Additionally, sensitivity analysis is conducted to assess the impacts of key parameters, such as carbon-trading prices, green-certificate-trading prices, and renewable energy quotas, on the systemic carbon emission behavior;

5. An improved version of NSGA-III is proposed for multi-objective optimization in analyzing carbon emission behavior by incorporating simulated binary crossover (SBX) and normal distribution crossover (NDX).

Despite extensive research demonstrating the effectiveness of low-carbon technologies and market mechanisms in reducing carbon emissions in regional integrated energy systems (RIESs), existing literature lacks targeted studies from the perspective of carbon peaking. There is a dearth of analysis on the carbon emission behavior of RIESs constrained by the goal for achieving carbon peaking, a critical aspect for planning and constructing RIESs while meeting the requirements of carbon-peaking implementation. This research aims to address this gap and fill the existing knowledge void.

The research in this paper is organized into six chapters. Section 1 provides an overview of the current research status both domestically and internationally. Section 2 outlines the research framework and the main contributions of this paper. Section 3 delves into the prediction of the carbon-peaking implementation pathway in Shanghai using the STIRPAT model. Section 4 establishes three typical configurations of regional integrated energy systems (RIESs) and their optimization models. Section 5 explores the carbon emission behaviors of the three typical RIES configurations under the constraint of the carbon-peaking pathway. It also examines the carbon emission behavior across RIESs in four carbon reduction policy scenarios that utilize green certificates and carbon-trading mechanisms. Finally, Section 6 summarizes the research findings and outlines future directions for further work.

2. Related Work

2.1. Carbon-Peaking Implementation Pathway Prediction

With the announcement of the carbon-peaking target, researchers have dedicated significant efforts to explore the feasibility and implementation approaches of the carbon-peaking goal. This topic has evolved into an increasingly attractive research hotspot within the academic community. Existing literature predominantly conducts analyses at a macro level, examining the influence of various factors on carbon emissions from the perspectives of the country [9], provinces [10], and regions [11]. Specific scenario frameworks are established based on economic development goals [12] to predict future carbon emission pathways [13]. The most frequently considered influencing factors include the population, GDP, energy intensity, urbanization level, industrial structure, energy structure, energy consumption, and investment [14].

Regarding prediction methods, the STIRPAT method has garnered considerable interest as an effective analytical model [15]. STIRPAT originated from the IPAT model, initially proposed by Ehrlich et al. [16] to assess the impact of human economic activities on the natural environment. Subsequently, Dietz et al. [17] refined the IPAT framework and developed the STIRPAT model. Currently, the model is widely used to evaluate the impact of various influencing factors on the environment [18]. Recognizing the limitations of the original STIRPAT model in specific domains, most current studies have expanded the basic framework by introducing additional factors to enhance the model’s flexibility and reliability. Gani et al. [19] incorporated additional factors, such as economics, the population, and institutions, into the basic STIRPAT framework and studied the impact of fossil fuel consumption on the environment in 59 coal-consuming countries, 77 gas-consuming countries, and 96 oil-consuming countries from 2002 to 2016. Particularly, the adoption of an expanded STIRPAT model for predicting carbon emissions under carbon-peaking goals has become a current research focus [20]. Huang et al. [21] explored the influences of 16 socioeconomic factors on carbon emissions in different cities and predicted the timing and peak levels of carbon emissions in Shandong Province, China. Xie [22] used the STIRPAT-PLS framework and Monte Carlo simulations to establish a dynamic scenario simulation, observing the dynamic trajectory and possible values of the carbon intensity in 2030, as well as conducting a dynamic sensitivity analysis of the contributions of different influencing factors. Similarly, to study the impact of the transportation industry on carbon emissions, Wang et al. [23] integrated transportation factors into the original factor set consisting of the environment, economy, population, and technology, expanding the basic structure of STIRPAT and examining the influences of these factors on carbon emissions.

Moreover, a substantial body of literature has sought to apply the STIRPAT method within scenario frameworks to assess the feasibility and implementation pathways of carbon-peaking targets. For instance, Tian et al. [24] utilized an expanded STIRPAT model to predict carbon emissions in Chinese provinces from 1996 to 2018 in three development scenarios: high speed, steady speed, and low speed. The research findings unveiled the influence of factors such as per capita GDP, energy intensity, industrial structure, energy structure, and the total population on carbon emissions and the timing for reaching the peak. The results also indicated that owing to different economic development conditions, various provinces could anticipate distinctly different carbon emission pathways.

2.2. Carbon Emission Reduction in a Regional Integrated Energy System

Currently, research on carbon emission reduction integrated with energy systems can be conducted at two levels: technological and market mechanisms.

2.2.1. Technological Level

The regional integrated energy system is built on the foundation of a multi-energy network and is regarded as a paradigmatic sustainable energy structure for achieving carbon-peaking goals [25]. It provides an intelligent approach to plan and integrate low-carbon technologies and various forms of energy throughout the entire region, efficiently coordinating and managing the energy supply and demand to minimize carbon emissions. Numerous studies have concentrated on the emission reduction aspects of regional integrated energy systems. Alavije [26] examined the cost effectiveness of various carbon strategies, such as increasing the utilization of biomass boilers, heat pumps, and storage units. The results demonstrated that a carbon reduction of 20.8% could be achieved with a modest increase of only 2.2% in related costs. Ma [27] established a combination of combined heat and power (CHP) with power-to-gas (P2G) and carbon capture and storage (CCS) units in an integrated energy system, showcasing its significant potential in promoting renewable energy adaptation, reducing carbon emissions, and saving operational costs. Pilpola [28] employed multiple clean energy technologies, including wind power, nuclear power, biomass, and electric energy heat exchange, to formulate low-carbon and carbon-neutral energy models for the Finnish nation and the city of Helsinki by 2025. The simulation results indicated that even without nuclear power, Finland’s energy model could achieve a carbon dioxide reduction of up to 94% when considering all the forest biomass potentials.

To validate feasible pathways for achieving peak carbon emissions by 2030 and carbon neutrality by 2050, Bamisile [29] employed 100% renewable energy, integrating wind power, hydropower, solar photovoltaics, biomass, pumped hydro storage, and clean electricity imports from other provinces to attain the maximum carbon emission reduction. Different scenarios were studied using the EnergyPLAN simulation platform. Additionally, with the introduction of carbon-peaking targets, an increasing number of studies have taken carbon emissions into consideration. A common approach is to integrate carbon reduction with other factors into an optimal objective to meet carbon reduction requirements. Wang et al. [30] proposed a new capacity allocation model for regional integrated energy systems that comprehensively considers the minimization of annual total costs and carbon emissions. Wang et al. [31] developed a multi-objective optimization model for the coupling of electricity, heating, and cooling in a regional integrated energy system based on economic, technical, and carbon reduction goals. The aim is to explore the system’s performance in terms of economics, autonomy, and carbon reduction. In addition to the research on individual regional integrated energy systems, there is also a focus on the combined optimization analysis of cross-regional integrated energy systems. Liao [32] constructed a decomposition strategy for neighboring regional integrated energy systems interconnected by a flexible direct current (DC), and the study demonstrated that multi-area interconnection is beneficial for improving energy utilization efficiency and reducing carbon emissions. Liu [33] established a cooperative game model for multi-area integrated energy systems based on Nash bargaining theory. Through simulation examples of a three-area integrated energy system, a comparative analysis in multiple scenarios showed that the proposed model significantly reduces economic costs while avoiding carbon emissions exceeding the standard.

2.2.2. Market Mechanism Level

To address the imperative for carbon emission reduction, various policies and measures utilizing market operation mechanisms have been proposed, leading to the rapid development of carbon-trading and green-certificate-trading mechanisms. The fundamental principle of carbon trading involves buyers, unable to meet carbon emission allowances independently, purchasing carbon emission rights from sellers to offset their shortfall, or else facing penalties. Green certificate trading entails the government issuing green-trading certificates for renewable energy generation by enterprises, which can be bought and sold among energy companies. There have been numerous studies focusing on carbon reduction within the context of regional integrated energy systems under carbon-trading and green-certificate-trading mechanisms. Zhang et al. [34] conducted a feasibility study on park-level integrated energy systems, incorporating distributed wind power, rooftop photovoltaics, and high-capacity wind power technologies. The analysis considered the carbon-trading mechanism, revealing that high-capacity wind systems have significant emission reduction effects. All the regional integrated energy systems achieved higher profits compared to those not participating in the carbon-trading mechanism. Yan et al. [35] proposed a low-carbon multi-energy virtual power plant model that took the impact of the carbon-trading mechanism into account. Simulation results demonstrated that carbon-trading revenue could notably reduce daily dispatching costs by 20.00% and decrease carbon emissions by 19.21% compared to traditional economic dispatch models. Yang et al. [36] introduced a dispatch optimization method for regional integrated energy systems, aiming to ensure a balanced and stable energy supply while considering the impact of carbon trading.

The method established a daily optimization dispatch model for a regional electrical and gas integrated energy network, incorporating a priority model for multi-energy devices. The objective function included the sum of energy output costs, energy conversion costs, and carbon-trading costs resulting from net carbon emissions. The optimization aims to achieve the optimal output of different forms of energy, thereby reducing energy costs and carbon emissions under the carbon-trading mechanism. Suo et al. [37] developed a two-stage fuzzy-logic-based stochastic programming method for energy system planning in Shanghai, introducing the carbon-emission-trading mechanism and green-certificate-trading mechanism. The experimental results demonstrated that the synergistic effect of the carbon-trading and green-certificate-trading mechanisms could transform Shanghai’s energy structure to a clean production mode and effectively reduce carbon emissions. Zeng et al. [38] combined the carbon-trading and green-certificate-trading mechanisms, incorporating the carbon-trading costs and green-certificate-trading costs of the park’s electric–thermal coupling system into the objective function. They proposed a planning model for the park’s integrated energy systems with distributed photovoltaic access. The results showed that the carbon-trading mechanism and green-certificate-trading mechanism could effectively promote the low-carbon transformation of the park. Yuan et al. [39] took into account the challenges of wind power integration and established a “source–load” bilateral complementary coordinated optimization dispatch model based on green certificates and in combination with the carbon-trading mechanism. Using an adaptive immune vaccine algorithm to solve the model, the results showed that the combination of carbon-trading and green-certificate-trading mechanisms facilitates wind power consumption and reduces the carbon intensity.

3. Research Framework and Main Contributions

The research framework of this study is illustrated in Figure 1. Using a specific building cluster area in Shanghai, China, as an example, the study analyzes the carbon emission behavior of the RIES under the goal for achieving carbon peaking from both macro and micro perspectives. At the macro level, the study predicts the carbon-peaking implementation pathway for Shanghai during the time window of 2020–2040. At the micro level, employing multi-objective optimization and considering the carbon-peaking implementation pathway as a constraint on carbon emissions, this study analyzes the carbon emission behaviors of three typical RIES configurations. Additionally, it examines the cross-RIES carbon emission behavior under the carbon-trading and green-certificate-trading mechanisms.

4. Prediction of Shanghai’s Carbon-Peaking Implementation Path Based on STIRPAT Model

4.1. Extension of the STIRPAT Model Construction

The STIRPAT model is capable for capturing the comprehensive impact of social, economic, and technological driving factors on carbon emissions. Its basic expression is as follows:

$$I=a{P}^b{A}^c{T}^{d}u$$

(1)

In the equation, I, P, A, and T represent the carbon emissions, population factor, wealth factor, and technological level, respectively; a represents the model coefficient; b represents the population elasticity coefficient; c represents the wealth elasticity coefficient; d represents the technological elasticity coefficient; and u represents the model’s error term. To comprehensively consider the influences of the population size, per capita GDP, urbanization rate, industrial structure, energy structure, and energy intensity on carbon emissions in Shanghai, this study introduces these factors to the basic STIRPAT model and constructs a multivariate nonlinear model as follows:

$$I=a{P}^b{A}^c{U}^d{IS}^e{ES}^f{EI}^g u$$

(2)

In the equation, the meanings of the variables are shown in

Table 1. Extension of variables in the STIRPAT model.

Variable	Explanatory Note	Unit
Carbon emissions (I)	Total carbon dioxide emissions	Million tons
Population size (P)	Population at the end of the year	${10}^4$
GDP per capita (A)	GDP per capita	Ten thousand CNY per person
Urbanization rate (U)	Urban population/total population	%
Industrial structure (IS)	Gross domestic product of the secondary sector/gross domestic product	%
Energy structure (ES)	Coal consumption/total fossil fuel consumption	%
Energy intensity (EI)	Total consumption of fossil energy/gross domestic product	Tons of standard coal per ten thousand CNY

. The coefficient a represents the model coefficient, while b, c, d, e, f, and g represent the elasticities of the corresponding variables.

Taking the logarithm of both sides of Equation (2) yields the following linear model:

$$lnI=ln\left(a+u\right)+blnP+clnA+dlnU+elnIS+flnES+glnEI$$

(3)

Considering the existence of the environmental Kuznets curve (EKC) [40], this study includes the quadratic term of the per capita GDP in the model. Thus, the STIRPAT model is further extended to incorporate this term as follows:

$$lnI=ln\left(a+u\right)+blnP+c_{1}lnA+c_2{\left(lnA\right)}^2+dlnU+elnIS+flnES+glnEI$$

(4)

4.2. The Scenario and Setting of Influencing Factors

In alignment with China’s and Shanghai’s development plans and relevant policies, three distinct scenarios—rapid, steady, and green developments—have been established for the rates of change in the population size, per capita GDP, urbanization rate, industrial structure, energy structure, and energy intensity. Within these scenarios, the rapid development scenario prioritizes economic growth as the primary objective; the green development scenario emphasizes carbon emission reduction, while the steady development scenario represents a moderate level of change in the rates of all the influencing factors.

Table 2. The setting of the three scenarios for the period 2020–2040.

Factor	Scenario	Policy Basis	2020–2025	2026–2030	2031–2035	2036–2040
$P$	Rapid	“Shanghai Urban Master Plan (2017–2035)”	0.76%	0.36%	−0.04%	−0.44%
	Steady		0.54%	0.34%	0.14%	−0.06%
	Green		0.42%	0.32%	0.22%	0.12%
$A$	Rapid	“14th Five-Year Plan and Long-Term Goals for 2035 of Shanghai’s National Economic and Social Development”	5.5%	4.5%	3.5%	2.5%
	Steady		5%	4%	3%	2%
	Green		4.5%	3.5%	2.5%	1.5%
$U$	Rapid	“13th Five-Year Plan of China”	0.8%	0.6%	0.4%	0.2%
	Steady		0.4%	0.3%	0.2%	0.1%
	Green		0.2%	0.15%	0.1%	0.05%
$IS$	Rapid	“13th Five-Year Plan of China”	−4%	−3.7%	−3.4%	−3.1%
	Steady		−4.5%	−4.2%	−3.9%	−3.6%
	Green		−5%	−4.7%	−4.4%	−4.1%
$ES$	Rapid	“Shanghai Energy Development’s 14th Five-Year Plan”, “Shanghai Carbon-Peaking Implementation Plan”, “Shanghai Statistical Yearbook”	−3.7%	−3.2%	−2.7%	−2.2%
	Steady		−4.2%	−3.7%	−3.2%	−2.7%
	Green		−4.7%	−4.2%	−3.7%	−3.2%
$EI$	Rapid	“China’s 14th Five-Year Plan for a Modern Energy System”, “China’s 13th Five-Year Plan”	−4.5%	−3.5%	−2.5%	−1.5%
	Steady		−5%	−4%	−3%	−2%
	Green		−5.5%	−4.5%	−3.5%	−2.5%

succinctly outlines the annual average growth rates of all the influencing factors and the policies formulated based on these scenarios for the period 2020–2040.

4.3. Annualized Data of Influencing Factors

The total consumption of fossil energy in Shanghai, the focus of this study, is sourced from the “China Energy Statistical Yearbook”. The population size, urbanization rate, per capita GDP, gross domestic product (GDP), and gross industrial output value of the secondary sector in Shanghai are extracted from historical editions of the “Shanghai Statistical Yearbook”. Additionally, the urbanization rate is gathered from historical editions of the “China Statistical Yearbook”. Carbon emissions are calculated based on total energy consumption using the carbon emission coefficient method (IPCC), and any missing data are addressed through interpolation. The annualized values of each influencing factor are summarized in

Table 3. Annualized values of each influencing factor.

	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
$I$	150.04	161.47	169.47	184.55	198.65	214.27	231.58	252.31	266.31	270.49	283.91	290.72	293.04	301.84	294.90	302.95	311.58	315.46	317.46	324.18
$P$	1608.60	1668.33	1712.97	1765.84	1834.98	1890.26	1964.11	2063.58	2140.65	2210.28	2302.66	2347.46	2380.43	2415.15	2425.68	2415.27	2419.70	2418.33	2423.78	2428.14
$A$	30,307	32,089	34,277	39,117	44,998	49,377	54,996	63,951	69,154	72,363	79,396	86,061	90,127	96,773	104,402	111,081	123,628	136,109	148,744	157,279
$U$	74.60	75.30	76.40	77.60	81.20	84.50	85.80	86.80	87.50	88.30	88.90	89.30	89.80	90.00	90.30	87.60	87.90	87.70	88.10	89.30
$IS$	46.00	45.90	45.50	47.60	47.80	46.90	46.50	44.10	42.80	39.30	41.50	40.80	38.40	35.70	34.20	31.30	28.70	28.90	28.80	27.00
$ES$	58.48	56.60	54.69	52.82	49.61	46.08	41.39	38.58	38.24	36.55	37.47	38.93	35.85	35.77	31.55	29.66	28.21	27.57	26.96	25.88
$EI$	1.14	1.11	1.06	0.98	0.91	0.89	0.84	0.75	0.70	0.66	0.63	0.56	0.53	0.49	0.44	0.42	0.39	0.36	0.33	0.31

Units: I—million tons; P—104; A—CNY/person; EI—tce/104 CNY; IS—%; ES—%; U—%.

.

4.4. Regression Results

Utilizing the annualized values of the influencing factors, ridge estimation is employed for regression analysis on the extended STIRPAT model, addressing the issue of multicollinearity among these factors. After confirming that the model tends to stabilize at k = 0.1, indicating a good overall fit, the fitting results of Equation (4) are presented in

Table 4. Results of regression fit for the extended STIRPAT model.

Factor	$\mathit{l}\mathit{n}\mathit{P}$	$\mathit{l}\mathit{n}\mathit{A}$	${\mathbf{\left(}\mathit{l}\mathit{n}\mathit{A}\mathbf{\right)}}^{\mathbf{2}}$	$\mathit{l}\mathit{n}\mathit{U}$	$\mathit{l}\mathit{n}\mathit{I}\mathit{S}$	$\mathit{l}\mathit{n}\mathit{E}\mathit{S}$	$\mathit{l}\mathit{n}\mathit{E}\mathit{I}$	$\mathit{l}\mathit{n}\mathbf{(}\mathit{a}\mathbf{+}\mathit{u}\mathbf{)}$
Ridge regression coefficients	0.4734	0.1693	0.006	0.8861	0.1213	0.1657	0.0582	−5.6391

.

Based on the regression results, the projected paths for carbon peaking in Shanghai from 2020 to 2040 in the three scenarios of rapid, steady, and green developments are predicted and illustrated in Figure 2. Notably, all three scenarios demonstrate a trajectory leading to carbon emission peaks. In the rapid development scenario, the peak is reached in 2035, and it is also the highest, reaching 377.45 million tons. These findings suggest that excessive economic growth would significantly elevate energy consumption and carbon emissions, contradicting the pursuit of harmonious development between humans and nature. In the steady and green development scenarios, the carbon emission peaks are lower and achieved in 2030 and 2025, respectively. This highlights the feasibility of carbon neutrality goals. Overall, Shanghai is advised to reinforce the development of low-carbon technologies and sustainable energy structures, particularly through the integration of renewable and traditional energy sources in the RIES energy supply model. This approach is crucial to effectively curb the excessive increase in carbon emissions and realize the objectives of the “dual carbon” strategy.

5. Model

5.1. Three Typical RIES System Configurations

China’s existing energy structure is predominantly coal dependent, emphasizing the critical necessity to advance the production and supply of clean and low-carbon energy to foster a sustainable and eco-friendly economy. Therefore, leveraging the penetration level of renewable energy, this paper formulates three representative RIES system configurations: fossil-based (FP-RIES), hybrid (HP-RIES), and clean (CP-RIES). These three frameworks are regarded as organizational paradigms for RIES in a broader context.

5.1.1. FP-RIES

Because of the substantial demands for large-scale investments and a dependable supply, a significant number of fossil-fuel-based power plants still exist in China. FP-RIES epitomizes the prevailing mode of an energy supply reliant on fossil fuels and serves as a comparative reference for the other two types of RIES. The structure of FP-RIES, illustrated in Figure 3, encompasses coal-fired power generation units, coal-fired boilers, and electric refrigeration units, each catering to the needs for electricity, heat, and cooling.

5.1.2. HP-RIES

Over the past few decades, combined cooling, heating, and power (CCHP), with an integrated supply of electricity, heat, and cold, has emerged as one of the most popular and promising low-carbon technologies in the energy industry. CCHP facilitates the cascading utilization of waste heat, leading to a significant enhancement in the overall energy utilization efficiency [41].

The structure of HP-RIES, as depicted in Figure 4, places the emphasis on the integration of renewable energy and CCHP for the energy supply, highlighting the importance for achieving coordination and balance between cost effectiveness and environmental considerations.

In HP-RIES, the integrated equipment comprises photovoltaics, wind power, solar collectors, biogas generators, internal combustion engines (ICEs), biogas and electricity storage devices, gas boilers, waste heat boilers, electric refrigeration units, and lithium bromide absorption chillers (LBACs), with ICE serving as the prime mover for combined cooling, heating, and power (CCHP). Taking into account the performance characteristics of CCHP, the entire system adopts two operating strategies: full electric load (FEL) and full thermal load (FTL).

Under the FEL strategy, the system initially fulfills the electricity demand utilizing photovoltaics, wind power, electric storage, biogas generators, and CCHP generation, supplemented by grid electricity when needed. Excess electricity generated by photovoltaics and wind power is stored, while the heat demand is met through solar collectors, waste heat from biogas generators and CCHP, and supplemented by a gas boiler if necessary. The cooling demand is addressed by LBAC units through thermal energy conversion supplemented by electric refrigeration units. In the FTL strategy, the system operates based on prioritizing the thermal load. The heat demand is satisfied by solar collectors and waste heat from biogas generators and CCHP and is supplemented by a gas boiler. Cooling is achieved through the thermal energy conversion of LBAC units. The electricity demand is met by photovoltaics, wind power, and CCHP, with unused electricity stored in electric energy storage devices and supplemented by grid electricity. In both strategies, the supply of the biogas is limited to a fixed daily production.

5.1.3. CP-RIES

The CP-RIES structure is designed to maximize the utilization of renewable energy technologies. Its structure is shown in Figure 5. In contrast to the HP-RIES structure, CP-RIES eliminates the combined cooling, heating, and power (CCHP) unit. The system incorporates renewable resources, such as solar energy, wind energy, and biogas, while still depending on grid electricity to ensure a reliable supply. For thermal supplementation, the system employs electric boilers instead of gas boilers. The CP-RIES maintains two distinct operating strategies: full electric load (FEL) and full thermal load (FTL).

5.2. Objective Function and Decision Variables

• Optimization model for regional integrated energy systems under carbon-peaking constraints;

To scrutinize the carbon emission behaviors of the three typical RIES system configurations under carbon-peaking constraints, a multi-objective optimization model is formulated with the objective for minimizing the system cost, carbon emissions, and primary energy consumption.

2.

Optimization model for cross-regional integrated energy systems under carbon-peaking constraints based on carbon-trading and green-certificate-trading mechanisms;

By incorporating carbon-trading and green-certificate-trading mechanisms, the analysis delves into the carbon emission behavior of a cross-regional integrated energy system comprising the three typical RIES system configurations under carbon-peaking constraints. To achieve this, a multi-objective optimization model is devised, aiming to minimize the system costs for each RIES system configuration and the overall carbon penalty cost for the entire region.

Table 5. Device lifetimes, unit costs, installed-capacity constraints, and operating characteristic constraints.

Device	Lifecycle (Years)	Unit Investment Cost (104 CNY/kW)	Installed-Capacity Constraint (kW)	Minimum Output Power (kW)	Conversion Efficiency/Self-Loss Coefficient
PV	25	0.8	[100, 3000]	10	-
SC	25	0.8	[100, 3000]	10	-
WINDP	18	1.2	[100, 3000]	10	-
ICE	20	0.36	[100, 5000]	10	-
BIOG	20	0.36	[100, 5000]	10	0.29/0.56
WHB	25	0.22	[200, 5000]	10	0.75
GB	20	0.35	[200, 8000]	10	0.85
EB	15	0.32	[200, 8000]	10	0.95
LBAC	30	0.12	[200, 5000]	10	1.15
EC	15	0.11	[100, 5000]	10	4.5
ESTOR	6	0.08	[500, 3000] kWh	20 kWh	0.05
GSTOR	15	0.12	[20, 1000] m3	-	0.05
TOU power price		Peak price (6:00–22:00)—0.894 CNY/kWh
		Valley price (23:00–next day at 5:00)—0.417 CNY/kWh
Natural gas price		4.47 CNY/m3

(Data source: the lifecycle, unit investment cost, and minimum output power data are from [42] and internet materials. The installed-capacity constraints are assumed based on research needs. Time-of-use electricity prices and natural gas prices are determined by the Shanghai Development and Reform Commission).

furnishes the equipment information, while

Table 6. Objective functions and decision variables for RIES and cross-RIES optimization models.

	Decision Variable	Objective Function	Variable Value
RIES optimization	$V_H=[P_H^{PV},P_H^{SC},P_H^{WINDP},P_H^{ICE}]$ $V_C=[P_C^{PV},P_C^{SC},P_C^{WINDP}]$	$ATC=C_{invest}+C_{consume}+C_{operation}$ $C_{invest}={\sum }_{k=1}^K{R}_s{C}_s$$, R_k={\displaystyle \frac{r(r+1)N_k}{\left(1+r\right)N_s-1}}$ $C_{consume}={\displaystyle \sum _{s=1}^S}{T}_s{\displaystyle \sum _{t=1}^{24}}(c_{gas}^t{V}_{ice}^t+c_{gas}^t{V}_{gb}^t+c_{power}^t{E}_{downgrid}^t)$ $C_{operation}=\lambda C_{invest}$	$\mathrm{r}=$6.7% $\lambda =$0.03;
		$CDE={\displaystyle \sum _{s=1}^S}{T}_s{\displaystyle \sum _{t=1}^{24}}({\lambda }_{gas}{V}_{gas}^t+{\lambda }_{BIOG}{V}_{BIOG}^t+{\lambda }_{power}{E}_{downgrid}^t)$	${\lambda }_{gas}=2.09 \mathrm{k}\mathrm{g}/{\mathrm{m}}^3$ ${\lambda }_{BIOG}=1.1725 \mathrm{k}\mathrm{g}/{\mathrm{m}}^3$ ${\lambda }_{power}=1.0302 \mathrm{k}\mathrm{g}/\mathrm{k}\mathrm{w}\mathrm{h}$
		$PEC={\displaystyle \sum _{s=1}^S}{T}_s{\displaystyle \sum _{t=1}^{24}}\left({\beta }_{gas}\right(V_{gt}^t+V_{ice}^t)+{\beta }_{BIOG}{V}_{BIOG}^t+{\beta }_{power}{E}_{downgrid}^t)$	${\beta }_{gas}=1.33\mathrm{t}\mathrm{c}\mathrm{e}/\mathrm{10,000} {\mathrm{m}}^3$ ${\beta }_{BIOG}=0.8286\mathrm{t}\mathrm{c}\mathrm{e}/\mathrm{10,000} {\mathrm{m}}^3$ ${\beta }_{power}={\displaystyle \frac{0.404\mathrm{t}\mathrm{c}\mathrm{e}}{\mathrm{10,000}}} \mathrm{k}\mathrm{W}\mathrm{h}$
Cross-RIES optimization	$V=\left[V_F,V_H,V_C\right]$ $V_F=[{\alpha }_F,{\beta }_F,{\gamma }_F]$ $V_H=[{\alpha }_H,{\beta }_H,{\gamma }_H,P_H^{PV},P_H^{SC},P_H^{WINDP},P_H^{ICE}]$ $V_C=[{\alpha }_C,{\beta }_C,{\gamma }_C,P_C^{PV},P_C^{SC},P_C^{WINDP}]$	${ATC}^{F,t}={ATC}^F+{\beta }_{CET}{C}_{trade}^F+{\beta }_{GC}{(C}_{gre}^F+{punish}_{gre}{GRE}_{lack}^F)$	-
		${ATC}^{H,t}={ATC}^H+{\beta }_{CET}{C}_{trade}^H+{\beta }_{GC}{(C}_{gre}^H+{punish}_{gre}{GRE}_{lack}^H)$	-
		${ATC}^{C,t}={ATC}^C+{\beta }_{CET}{C}_{trade}^C+{\beta }_{GC}{(C}_{gre}^C+{punish}_{gre}{GRE}_{lack}^C)$	-
		$C_{punish}={punish}_{carbon}{CDE}_{excess}$	-

outlines the objective functions and decision variables for the optimization models of the RIES and cross-RIES.

In the optimization of the RIES, the decision variables for HP-RIES comprise the installed capacities of photovoltaics, solar collectors, wind turbines, and gas turbines. For CP-RIES optimization, the decision variables encompass the installed capacities of photovoltaics, solar collectors, and wind turbines. In the cross-RIES optimization, the decision variables encompass the installed capacities of each RIES system and the ratio of the thermal and electrical loads assigned to each RIES system.

5.3. Constraints

Both the RIES and cross-RIES optimization models are bound by energy balance constraints among the devices. Moreover, owing to specific installation conditions, the installed capacities and output powers of the devices are subjected to upper and lower limits, specifically:

$$E_D\left(t\right)+P_{EC}\left(t\right)=E_{PV}\left(t\right)+E_{WINDP}\left(t\right)+E_{BIOG}\left(t\right)+E_{ICE}\left(t\right)+E_{GRID}\left(t\right)+E_{ESTOR}\left(t\right)$$

(5)

$$H_D\left(t\right)=H_{WHR}\left(t\right)+H_{GB}\left(t\right)$$

(6)

$$C_D\left(t\right)=C_{EC}\left(t\right)+C_{LBAC}\left(t\right)$$

(7)

$$C_k^{\min }\le C_k\le C_k^{\max }$$

(8)

$$P_k^{min}\le P_k\le P_k^{max}$$

(9)

5.4. Improved NSGA-III

The improved non-dominated sorting genetic algorithm III (NSGA-III) stands out as one of the most representative algorithms for solving high-dimensional multi-objective optimization problems. Diverging from the traditional crowding distance calculation, this algorithm adopts a reference-point-based approach to effectively enhance the diversity of the population. In the base NSGA-III algorithm, the crossover operator is implemented using the simulated binary crossover (SBX), known for its lower search efficiency [43]. In our prior research, an adaptive hybrid crossover operator named NDX-SBX, as depicted in Equation (10) [44], was proposed. This adaptive operator combines SBX with the normal distribution crossover (NDX). Initially, a higher proportion of the SBX operator is employed to maximize the search range of the population. As the algorithm progresses, a higher proportion of the NDX operator is utilized to enhance the search accuracy.

$$\left\{\begin{array}{c}{\mathrm{x}}_{1,\mathrm{j}}=\left\{\begin{array}{c}{\displaystyle \frac{\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{s}-\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}}{2\ast \mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{s}}}\left[\left(1+\mathsf{\alpha }\right){\mathrm{p}}_{1,\mathrm{j}}+\left.\left(1-\mathsf{\alpha }\right){\mathrm{p}}_{2,\mathrm{j}}\right]+{\displaystyle \frac{\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}}{2\ast \mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{s}}}\ast 1.481\ast \left[\left(1+\mathsf{\beta }\right){\mathrm{p}}_{1,\mathrm{j}}+\left.\left(1-\mathsf{\beta }\right){\mathrm{p}}_{2,\mathrm{j}}\right],\mathsf{\mu }\le 0.5\right.\right.\\ {\displaystyle \frac{\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{s}-\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}}{2\ast \mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{s}}}\left[\left(1+\mathsf{\alpha }\right){\mathrm{p}}_{1,\mathrm{j}}+\left.\left(1-\mathsf{\alpha }\right){\mathrm{p}}_{2,\mathrm{j}}\right]+{\displaystyle \frac{\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}}{2\ast \mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{s}}}\ast 1.481\ast \left[\left(1-\mathsf{\beta }\right){\mathrm{p}}_{1,\mathrm{j}}+\left.\left(1-\mathsf{\beta }\right){\mathrm{p}}_{2,\mathrm{j}}\right],\mathsf{\mu }>0.5\right.\right.\end{array}\right.\\ {\mathrm{x}}_{2,\mathrm{j}}=\left\{\begin{array}{c}{\displaystyle \frac{\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{s}-\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}}{2\ast \mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{s}}}\left[\left(1-\mathsf{\alpha }\right){\mathrm{p}}_{1,\mathrm{j}}+\left.\left(1+\mathsf{\alpha }\right){\mathrm{p}}_{2,\mathrm{j}}\right]+{\displaystyle \frac{\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}}{2\ast \mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{s}}}\ast 1.481\ast \left[\left(1+\mathsf{\beta }\right){\mathrm{p}}_{1,\mathrm{j}}+\left.\left(1-\mathsf{\beta }\right){\mathrm{p}}_{2,\mathrm{j}}\right],\mathsf{\mu }\le 0.5\right.\right.\\ {\displaystyle \frac{\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{s}-\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}}{2\ast \mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{s}}}\left[\left(1-\mathsf{\alpha }\right){\mathrm{p}}_{1,\mathrm{j}}+\left.\left(1+\mathsf{\alpha }\right){\mathrm{p}}_{2,\mathrm{j}}\right]+{\displaystyle \frac{\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}}{2\ast \mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{s}}}\ast 1.481\ast \left[\left(1-\mathsf{\beta }\right){\mathrm{p}}_{1,\mathrm{j}}+\left.\left(1-\mathsf{\beta }\right){\mathrm{p}}_{2,\mathrm{j}}\right],\mathsf{\mu }>0.5\right.\right.\end{array}\right.\end{array}\right.$$

(10)

6. Simulation and Analysis

6.1. Load and Climate Overview

This study focuses on a building cluster within the Shanghai Economic Zone as the load terminal for the analysis. The cluster comprises office buildings, hotels, and commercial centers, with the commercial center spanning 26,000 square meters, the hotel covering 15,000 square meters, and the office buildings occupying an area of 36,000 square meters. The electricity, heat, and cooling demands of the building cluster in 2019 are simulated and modeled using Dest 2.0 software. Considering the seasonal and climatic characteristics of Shanghai, the entire year’s load is segmented into three typical days: summer, winter, and transitional seasons, utilizing the K-means clustering method. Additionally, to compare the output levels of solar and wind energies, the performance coefficients of photovoltaics, solar collectors, and wind turbines are calculated based on the solar radiation, ambient temperature, and wind speed on typical days in Shanghai.

Given the location of the building cluster in the Shanghai Economic Zone, its load demand is closely tied to the GDP. Therefore, the three scenarios of rapid, steady, and green developments, the average annual load demand growth rates for the years 2020–2040 are set, as indicated in

Table 7. Load growth rates for a specific region in Shanghai, 2020–2040.

Scenario	2020–2025	2026–2030	2031–2035	2036–2040
Rapid development	5%	4.5%	4%	3.5%
Steady development	4%	3.5%	3%	2.5%
Green development	3%	2.5%	2%	1.5%

.

6.2. Carbon Emission Constraints for the Carbon-Peaking Implementation Pathway

In Section 4, the carbon-peaking implementation pathway is projected in various development scenarios, taking into account Shanghai’s policies and historical data. This pathway serves as the carbon emission constraint for scrutinizing the carbon emission behaviors of RIES and cross-RIES systems. However, the anticipated carbon-peaking pathway is applicable to the entire Shanghai area, and the estimated carbon emissions for the mentioned building cluster region are disproportionately high, necessitating a scaling-down adjustment.

Utilizing the load demand data from 2019, the aggregate heating, cooling, and electricity loads of the building cluster region, as simulated using Dest 2.0 software, are considered as electricity loads and transformed to standard coal consumption based on the equivalent thermal power output. Subsequently, the carbon emissions for the building cluster region in 2019 are calculated using the IPCC-prescribed standard coal carbon emission factor (0.7599 tons/ton of standard coal). The calculated carbon emissions for the building cluster region in 2019 amounted to 15,479.7 tons. The proportion (k) of the building cluster region’s carbon emissions to the total carbon emissions of Shanghai in the same year was calculated at $4.77\times {10}^{-5}$. Based on this proportion, the predicted carbon emissions in the carbon-peaking implementation pathways for the rapid, steady, and green development scenarios are scaled down to the scale of the building cluster region. Additionally, because Dest software only considers thermal power generation and does not account for the impact of renewable energy integration on carbon emissions, the National Energy Administration’s report on the monitoring and evaluation of renewable energy power generation in 2019 indicated a renewable energy generation ratio (${\beta }_{renewable}$) of 0.279. Taking these factors into account, the carbon emission constraint index $(C_{limit}$) for the building cluster region in the rapid, steady, and green development scenarios can be calculated as follows:

$$C_{limit}=k{\beta }_{renewable}{C}_{predict}$$

(11)

For carbon-peaking implementation, the carbon emission constraints of the building cluster region from 2020 to 2040 in the three scenarios are illustrated in Figure 6. The maximum emission limits for the rapid, steady, and green development scenarios are 5028.46 tons in 2035, 4563.96 tons in 2030, and 4349.36 tons in 2025.

6.3. Results and Analysis

6.3.1. Carbon Emission Analysis

Using FP-RIES as a reference, the carbon emissions are calculated based on the load demands in the rapid, steady, and green development scenarios, as depicted in Figure 7. The growth rates of the carbon emissions in all three scenarios closely mirror the load growth rates. The highest-emission scenario is the rapid development scenario, reaching 11,266.7 tons in 2040, followed by the steady-development scenario (10,840.6 tons) and the green-development scenario (10,426.7 tons). Upon comparing Figure 7 with Figure 6, it becomes evident that the carbon emissions of FP-RIES in all three scenarios are more than double the carbon emission constraints. Because FP-RIES relies entirely on fossil fuels without any low-carbon technologies, it results in uncontrolled carbon emissions. It is clear that relying on FP-RIES for the energy supply cannot meet the low-carbon requirements for achieving carbon-peaking goals.

Figure 8 presents the carbon emissions of HP-RIES and CP-RIES under different operating strategies of FEL and FTL, both with and without carbon emission constraints. Optimization is conducted for both CP-RIES and HP-RIES with and without carbon emission constraints, as indicated in

Table 8. HP-RIES and CP-RIES carbon emissions in the green development scenario.

Carbon Emissions (Tons)	2020	2025	2030	2035	2040
With constraints	2308.151	2400.674	2666.228	2939.199	3305.261
Without constraints	2308.158	2400.675	2666.228	2939.196	3305.241

and

Table 9. The carbon emissions of HP-RIES and CP-RIES.

		Rapid Scenario (Tons)		Steady Scenario (Tons)		Green Scenario (Tons)
		2020	2040	2020	2040	2020	2040
Constraint	HP-RIES-FEL	3721	5201	3721	4712	3721	4378
		−29.5%	−11.8%	−29.5%	−16.88%	−29.5%	−20.39%
	HP-RIES-FTL	5278	5897	5278	5669	5278	5499
No constraint	HP-RIES-FEL	3422	4887	3322	4152	3222	3920
		−30.26%	−14.02%	−31.6%	−25.64%	−32.83%	−28.73%
	HP-RIES-FTL	4907	5684	4857	5584	4797	5500
	CP-RIES-FEL	2308	3819	2296	3409	2284	3305
		−30.63%	−16.67%	−30.51%	−20.44%	−30.56%	−16.14%
	CP-RIES-FTL	3327	4583	3304	4285	3289	3941

and Figure 8. Table 8 displays the carbon emissions of CP-RIES in the “electricity-based heating” mode in the green development scenario from 2020 to 2040. It is evident that the carbon emission constraints have no impact on CP-RIES because of its highest renewable energy penetration, and Figure 8 illustrates that its carbon emissions remain below the carbon emission constraints set by the carbon-peaking pathway in both the FEL and FTL operating modes.

From Table 9 and Figure 8, it is clear that carbon emission constraints significantly influence the environmental performance of HP-RIES in all the scenarios. The emissions are notably lower when there are carbon constraints compared to when there are no constraints, indicating that carbon constraints have a suppressive effect on the emissions of the hybrid renewable energy and traditional fossil energy RIESs. Additionally, as confirmed by numerous other studies, the FEL and FTL operating modes determine different energy-supply-scheduling methods, thereby directly affecting the environmental performance of the energy system [45,46]. The optimization results reveal that in all three development scenarios, the carbon emissions of HP-RIES and CP-RIES in the FEL mode are significantly lower than those in the FTL mode. The negative percentages described in Table 8 indicate the reduction level of FEL relative to FTL, reaching over 30% at the maximum.

Figure 8 provides a comprehensive illustration of the impacts of the carbon emission constraints, RIES system configuration, and operating modes on carbon emissions in the scenarios of rapid, steady, and green developments. It is evident that as carbon emission constraints progressively strengthen in these scenarios, the carbon emissions of all the RIES systems exhibit a decreasing trend. This suggests that the carbon emission constraints, as defined by the carbon-peaking implementation pathway, have a significant suppressive effect on carbon emissions. Furthermore, the emissions of HP-RIES in the FTL mode are notably higher than the carbon emission constraint line. Even with carbon emission constraints, they cannot meet the carbon-peaking target. In contrast, HP-RIES in the FEL mode can achieve the carbon-peaking target under carbon emission constraints, and without constraints, it remains below the constraint line until 2032, gradually surpassing it later. An intriguing observation is the changing relative positions of the emissions of HP-RIES in the FEL mode and CP-RIES in the FTL mode in different scenarios for carbon emission constraints. In the rapid development scenario, the former is significantly higher than the latter; in the steady development scenario, they intersect; and in the green development scenario, the former gradually becomes lower than the latter. This suggests that as carbon constraints strengthen, the environmental performance of HP-RIES in the FEL mode surpasses that of CP-RIES in the FTL mode.

6.3.2. Analysis of Marginal Abatement Costs

The marginal abatement cost refers to the economic cost required to reduce (or increase) one unit of carbon emission under a reasonable input–output allocation, reflecting the marginal utility of the resource allocation to the actual output. To further investigate the impact of carbon-peaking targets on RIESs, the marginal abatement costs of HP-RIES and CP-RIES in the FEL and FTL operating modes are calculated, and the results are illustrated in Figure 9. The findings indicate that in all three development scenarios, the FEL mode exhibits significantly lower marginal abatement costs compared to the FTL mode. Furthermore, in the same mode, HP-RIES has slightly lower marginal abatement costs compared to CP-RIES. The optimization results further confirm the superior environmental benefits of the FEL mode over the FTL mode in terms of operating mode selection. It is also observed that although HP-RIES has a lower renewable energy penetration compared to CP-RIES, it demonstrates stronger economic competitiveness. This suggests that although renewable energy has a better low-carbon performance, its economic costs are higher compared to those of traditional energy sources.

6.4. Cross-RIES Optimization Results and Analysis

To explore the role of market mechanisms in promoting carbon emission reduction in RIESs, this section establishes four scenarios of carbon reduction policies based on carbon-trading and green-certificate-trading mechanisms. Taking the green development scenario as an example, the carbon emission behavior of a cross-RIES consisting of three typical RIES system configurations is analyzed. This includes environmental indicators, such as carbon emissions, carbon intensity, and marginal abatement costs, as well as a sensitivity analysis of the effects of key parameters, such as carbon-trading prices, green-certificate-trading prices, and renewable energy quotas, on the system’s carbon emission behavior.

6.4.1. Green-Certificate-Trading Mechanism

The green-certificate-trading mechanism provides external subsidies to renewable energy through market-based transactions, alleviating the financial pressure of renewable energy price subsidies for achieving carbon-peaking targets. In the optimization of the cross-RIES system, excess renewable energy beyond the quota can be converted to green certificates and sold, while RIES systems that fail to meet the quota need to purchase green certificates to avoid penalties. The penalty price for a renewable energy quota shortfall (${punish}_{gre}$) is set at 10,000 CNY per unit.

There are two key parameters in the green-certificate-trading mechanism: the renewable energy quota and the green certificate price. To support the carbon-peaking targets, the renewable energy quota for each RIES system is set at 10% of the total electricity generation. Based on the first green certificate transaction of China’s parity project in 2021, the green certificate price is set at 50 CNY per unit, equivalent to 50 CNY per 1000 kWh of green electricity.

6.4.2. Carbon-Trading Mechanism

Carbon trading involves the market-based trading of carbon emission rights as commodities. Under this mechanism, when the actual carbon emissions of an RIES system are lower than the allocated carbon emission rights, the system can profit by selling the surplus carbon emission rights to other RIES systems. Conversely, if the system exceeds its carbon emission rights, it needs to purchase additional carbon emission rights to meet its carbon emission requirements. When the total carbon emissions of the entire cross-RIES system exceed the specified carbon emission rights, the entire system will face economic penalties. In this study, the carbon-trading price is based on the closing price of the Carbon Emission Allowance (CEA) listing agreement in the Chinese carbon market in 2023, set at 58 CNY per ton, and the carbon penalty price is set at 10,000 CNY per ton.

Based on the optimization results of the green development scenario in Section 6.3, the total carbon emission rights for the cross-RIES system are determined based on the sum of the carbon emissions of the three RIES system configurations. The initial carbon emission right quotas for each RIES system are set based on their respective proportions of carbon emissions within the total carbon emission rights. It is important to note that the carbon emission right allocation in this case adopts the grandfathering method, which uses historical emissions as a reference and has the advantages for being convenient and practical. The specific data can be found in

Table 10. Total carbon emission rights for the cross-RIES system and initial carbon emission right quotas for each sub-RIES in the green development scenario.

	2020	2025	2030	2035	2040
Total carbon emission rights for cross-RIES (tons)	4341	4349	4314	4234	4110
Carbon emission rights for fossil energy subsystem (tons)	2752	2721	2661	2558	2428
Carbon emission rights for hybrid energy subsystem (tons)	930	963	948	944	913
Carbon emission rights for clean energy subsystem (tons)	659	665	705	732	769

.

6.4.3. Four Carbon Emission Reduction Policy Scenarios

In order to comprehensively study the impacts of different carbon emission reduction policies, based on market mechanisms, on the carbon emission behaviors of cross-RIES systems, this chapter sets up four specific scenarios for analysis, as shown in

Table 11. Four carbon emission reduction policy scenarios.

Scenario	Carbon Emission Reduction Policy	${\mathit{\beta }}_{\mathit{G}\mathit{C}}$	${\mathit{\beta }}_{\mathit{C}\mathit{E}\mathit{T}}$
Reference-CRIES	None	0	0
GC-CRIES	Green-certificate-trading mechanism	1	0
CET-CRIES	Carbon-trading mechanism	0	1
GC-CET-CRIES	Green-certificate- combined with carbon-trading mechanism	1	1

. ${\beta }_{GC}$ and ${\beta }_{CET}$ represent the control variables for the green-certificate-trading mechanism and the carbon-trading mechanism, respectively, with a value of 1 indicating the application of the respective policy and a value of 0 indicating no application of the respective policy.

Figure 10 illustrates the simulated results of carbon emissions in the cross-RIES system in four carbon emission reduction policy scenarios in the context of green development. It compares the carbon emissions for HP-RIES and CP-RIES operating in the FEL mode, as discussed in Section 6.3.

The results in Figure 10 show that in the four carbon-emission reduction policy scenarios, the carbon emissions in the cross-RIES system are ranked from the highest to the lowest as Reference-CRIES, GC-CRIES, CET-CRIES, and GC-CET-CRIES. The Reference-CRIES scenario fails to achieve the carbon-peaking target, while in the other scenarios, the carbon emissions of the entire system are significantly lower than the carbon-peaking target constraint because of the market-driven effects of carbon- and green-certificate-trading mechanisms. This finding aligns with the results of another study in the literature [37]. Furthermore, it can be observed that the carbon emissions in the GC-CRIES scenario are higher than those of the HP-RIES system operating in the FEL mode. By 2040, the carbon emissions in the GC-CRIES scenario approach the total carbon emission rights. The CET-CRIES and GC-CET-CRIES scenarios, which implement the carbon-trading mechanism, outperform the HP-RIES system operating in the FEL mode in terms of the emission reduction performance. This indicates that compared to the carbon-trading mechanism, the green-certificate-trading mechanism has limited inhibitory effects on carbon emissions and weaker emission reduction performance. Additionally, the carbon emissions of CP-RIES operating in the FEL mode are lower than the carbon emissions of the cross-RIES system in the four carbon-emission reduction policy scenarios. This demonstrates the positive impact of market-based carbon emission reduction policies on traditional fossil energy emissions. Among all the carbon emission reduction policy scenarios, the GC-CET-CRIES scenario exhibits the best emission reduction performance. Although its carbon emissions are higher than those of CP-RIES operating in the FEL mode during the period from 2020 to 2035, by 2040, the GC-CET-CRIES scenario’s carbon emissions are lower than those of CP-RIES operating in the FEL mode. This indicates that with continued growth in the load demand, the cross-RIES system in the GC-CET-CRIES scenario has more significant emission reduction advantages. This is because as the load demand increases, CP-RIES is forced to use grid electricity, with poorer carbon abatement benefits, because of the fluctuating and limited nature of the renewable energy output, whereas carbon- and green-certificate-trading mechanisms can effectively adjust the allocation of the electricity, heating, and cooling loads among the RIES systems within the cross-RIES system. This allows for meeting both the carbon emission targets and load demand.

To further explore the impacts of the underlying mechanisms of carbon emission reduction policies on carbon reduction in the cross-RIES system, Figure 11 presents the load allocation rates and carbon emissions of each RIES system in the four carbon-emission reduction policy scenarios. It can be observed that with the implementation of carbon emission reduction policies, the load allocations of the individual RIES systems in the cross-RIES system shift from the predominantly fossil-fuel-based FP-RIES to the renewable-energy-based CP-RIES. Taking the year 2040 as an example, the load allocation rate of CP-RIES gradually increases in the carbon emission reduction policy scenarios, starting from 31.22% in the Reference-CRIES scenario to 34.01% in the GC-CRIES scenario, 37.2% in the CET-CRIES scenario, and 39.18% in the GC+CET-CRIES scenario. Correspondingly, the carbon emissions of FP-RIES decrease gradually to 31.34%, 27.42%, 25.07%, and 23.40% in the respective scenarios. This shift in the load allocation results in a significant reduction in carbon emissions for the entire system in the order of Reference-CRIES, GC-CRIES, CET-CRIES, and GC+CET-CRIES. It is evident that carbon- and green-certificate-trading mechanisms optimize the allocation of the load between renewable energy and traditional fossil energy sources through market mechanisms, thereby achieving the low-carbon nature of the cross-RIES system. This further confirms the superiority of carbon-trading mechanisms over green-certificate-trading mechanisms in terms of emission reduction. It is important to note that in the four carbon-emission reduction policy scenarios, the load allocation rate of the HP-RIES remains relatively stable, without any significant changes. This is because HP-RIES is a hybrid system, with both renewable and fossil energy sources, and the market mechanism plays a neutralizing role in load regulation within the HP-RIES system.

To analyze the impacts of carbon emission reduction policies on carbon emissions and economic costs, Figure 12 presents the variations in the carbon intensity and marginal abatement costs in the different market-based carbon emission reduction policy scenarios. It can be observed that the carbon intensity ranks from the highest to the lowest as the Reference-CRIES, GC-CRIES, CET-CRIES, and GC+CET-CRIES scenarios. Compared to the Reference-CRIES scenario, the GC+CET-CRIES scenario reduces the carbon intensity by 35.35%, 33.96%, 34.43%, 32.89%, and 31.36% in the years 2020, 2025, 2030, 2035, and 2040, respectively. It is worth noting that GC-CRIES exhibits a significantly higher carbon intensity compared to those of CET-CRIES and GC+CET-CRIES, while the difference between CET-CRIES and GC+CET-CRIES is minimal. As for the marginal abatement costs, the scenario without any policy intervention shows significantly higher costs, while the differences among the other three scenarios are relatively small, ranked from the highest to the lowest as GC+CET-CRIES, CET-CRIES, and GC-CRIES. Considering the above observations, it further confirms that carbon trading has a more significant emission reduction effect compared to green certificate trading, albeit with a slight increase in abatement costs.

6.4.4. Sensitivity Analysis

• Carbon-trading prices and green-certificate-trading prices;

Using a baseline value of 50 CNY per unit, the green-certificate-trading prices are set to vary in increments of ±25 CNY. A price of 0 CNY per unit represents a scenario considering only the carbon-trading mechanism, i.e., the CET-CRIES scenario. Using a baseline value of 58 CNY per ton, the carbon-trading prices are set to vary in increments of ±20 CNY. A price of 0 CNY per ton represents a scenario considering only the green-certificate-trading mechanism, i.e., the GC-CRIES scenario. The renewable energy quota is set at 10%. Figure 13 illustrates the impacts of variations in carbon-trading prices and green-certificate-trading prices on the system’s carbon emissions.

From the analysis, it can be observed that when the carbon-trading price is 0 CNY per ton, i.e., in the GC-CRIES scenario, as the green-certificate-trading price increases from 25 CNY per unit to 125 CNY per unit, the system’s carbon emissions decrease from 4105 tons to 3926 tons, resulting in a reduction of 4.36%. When the green-certificate-trading price is 0 CNY per unit, i.e., in the CET-CRIES scenario, as the carbon-trading price increases from 18 CNY per ton to 98 CNY per ton, the system’s carbon emissions decrease from 3524 tons to 3312 tons, resulting in a reduced rate of 6.02%. When the system transitions from the Reference-CRIES scenario (with both the carbon-trading and green-certificate-trading prices at 0) to the GC+CET-CRIES scenario (with a green-certificate-trading price of 125 CNY per unit and a carbon-trading price of 98 CNY per ton), the system’s emissions decrease from the maximum value of 4648 tons to the minimum value of 3081 tons, resulting in a reduction of 33.71%. It can be seen that the increase in prices for both mechanisms effectively suppresses the system’s carbon emissions, which is consistent with existing research findings [47,48,49], with the impact of carbon-trading price variations being more pronounced. The reason behind this can be attributed to the fact that HP-RIES and CP-RIES have the production capacity of renewable energy, making the constraint of the green certificate quota requirements relatively weaker for these system configurations.

This constraint primarily affects the FP-RIES system, which relies solely on traditional fossil energy.

Table 12. The load rate of the fossil energy subsystem changes at a renewable energy quota of 10%.

Green-Certificate-Trading Price (CNY/Unit)	0	25	50	75	100	125
Fossil energy subsystem load rate	31.34%	28.05%	26.40%	25.76%	25.23%	24.80%

illustrates the changes in the load allocation rates for FP-RIES when the renewable energy quota is set at 10%. It can be observed that an increase in green certificate prices leads the system to reduce its own load allocation in order to lower the cost for purchasing green certificates. On the other hand, variations in carbon-trading prices affect all the RIES systems. When carbon-trading prices increase, the entire system needs to actively promote low-carbon technologies and strive to reduce carbon emissions to avoid high carbon penalty costs.

2.

Green-certificate-trading prices and renewable energy quotas.

In this section, this study takes 10% as the baseline value and explores the impact of variations in renewable energy quotas within a range of ±2% on carbon emissions across RIES systems. The results are presented in Figure 14. With fixed green-certificate-trading prices of 25 CNY per unit, 50 CNY per unit, 75 CNY per unit, 100 CNY per unit, and 125 CNY per unit, as the renewable energy quota increases from 4% to 14%, the reduction rates of the system’s carbon emissions are 2.34%, 6.92%, 6.62%, 7.07%, and 7.15%, respectively. It can be observed that increasing the renewable energy quota enhances the environmental performance of the system, and this effect becomes more significant as the green-certificate-trading price rises. Therefore, it can be concluded that simultaneously increasing the green-certificate-trading price and the renewable energy quota will actively promote a reduction in the system’s carbon emissions.

7. Conclusions

The carbon emission behavior of the integrated regional energy system (RIES) under the carbon-peaking target is crucial for guiding system capacity configuration and implementing carbon reduction policies. Despite limited research in this area, this paper addresses this gap by analyzing the carbon emission behaviors of RIESs, using a building cluster in an economic zone of Shanghai as an example. Similar to a study by Huang et al. [21], our study emphasizes the role of renewable energy integration in reducing carbon emissions within RIES configurations. By employing an extended STIRPAT model, we predict the carbon-peaking trajectory in Shanghai, aligning with the work of Huang et al. [2], who also utilized enhanced models to assess socioeconomic impacts on emissions. The goal is to offer valuable insights for the construction and planning of the energy system in Shanghai. The following constructive conclusions can be drawn from this work:

• The predictive analysis of the extended STIRPAT model, considering the existence of the environmental Kuznets curve, reveals that in the three development scenarios of rapid, steady, and green developments established based on China’s and Shanghai’s development plans and relevant policies, the carbon-peaking target in Shanghai can be achieved as early as 2025 or as late as 2035;
• Three typical RIES system configurations are proposed based on different levels of renewable energy penetration. The analysis of the carbon emission behaviors of the RIES systems constrained by the carbon-peaking target reveals that the CP-RIES system, entirely powered by renewable energy, is not constrained by the carbon-peaking target and can meet the target on its own. However, even with the carbon-peaking target constraint, the FP-RIES system relying on traditional fossil energy cannot meet the carbon-peaking requirement. Additionally, a significant finding is that different operational modes have significant impacts on the system’s carbon emission behavior. The analysis results show that the FEL mode exhibits a significant low-carbon performance, while the FTL mode performs poorly in terms of carbon reduction, especially for the HP-RIES system when operated in the FTL mode, making it difficult to meet the carbon-peaking requirements;
• The analysis of the four carbon-reduction policy scenarios, based on carbon-trading and green-certificate-trading mechanisms, demonstrates that carbon reduction policies through market mechanisms can optimize the allocation of loads in different RIES systems, effectively suppressing the overall carbon emissions across RIES systems. The low-carbon impact of the carbon-trading mechanism is more significant compared to that of the green-certificate-trading mechanism, but it comes with relatively higher marginal abatement costs. Furthermore, the sensitivity analysis of key design parameters of carbon reduction policies reveals that the carbon-trading price, green-certificate-trading price, and renewable energy quota have significant impacts on the carbon emissions across RIES systems. Increasing these three factors will actively promote carbon emission reduction, with the carbon-trading price having a more pronounced effect.

This study has certain limitations. First, because of the diversity of low-carbon technologies, such as renewable energy, this research focuses on three typical RIES system configurations based on the penetration level of the renewable energy, which may have some limitations as the selections of renewable energy and low-carbon technologies are based on the energy structure characteristics of typical cities, like Shanghai. Second, the research data in this paper are derived from the literature and government departments, which may have some inaccuracies in terms of precision. However, these limitations do not affect the reliability of the research findings.

This paper centers on a specific building cluster in Shanghai and conducts a carbon emission analysis of different types of heterogeneous low-carbon technologies within the RIES region. It analyzes the carbon emission behavior of the Shanghai Regional Integrated Energy System (RIES) through the extended STIRPAT model and predicts the carbon-peaking path from 2020 to 2040. This study sets out a clear vision for rapid, robust, and green development, showing the different relationships between the economy and growing carbon emissions and utilizing fossil fuel, hybrid, and clean energy configurations to explore carbon emissions for different energy structures. These results show that market mechanisms, such as carbon trading and green certificate trading, can effectively reduce emissions, but the cost is high. The key lies in the setting of transaction prices and renewable energy losses. This provides valuable advice for realizing low-carbon technological transformation. Future research can consider breaking the boundaries of the RIES region and expanding the analysis to the entire city of Shanghai. This analysis can explore the heterogeneity of various low-carbon technologies in their contributions for achieving the carbon-peaking target and provide valuable insights for the low-carbon transformation of the energy structure in Shanghai.