Emission Reduction Rate Research Articles

Research on the Whole Lifecycle Emission Reduction Effect of Buildings with Different Structures in Severely Cold Regions—A Case Study in China

Since the construction industry is one of China’s high carbon emission industries, to achieve China’s carbon neutrality target by 2060, CO2 emissions in cold regions must be reduced. At the same time, forests have excellent carbon sequestration abilities, so this paper takes residential buildings in severely cold regions as the object of carbon emission reduction research. A model of a two-story building in Changchun was constructed, and the life-cycle carbon emissions of reinforced concrete and wood structures were measured using the life-cycle evaluation method as the basis for calculation and simulation with DesignBuilderVer.7 software. The results show that the life-cycle carbon emission of a wood structure house is 61.46 t less than that of a reinforced concrete house, and the life-cycle carbon emission reduction rate of a wood structure house is 43.39%. Based on the data, it has been proven that wooden structures effectively reduce carbon dioxide emissions during the building life cycle while enhancing building performance, given the same structural conditions.

Preparation and Properties of Low-Carbon Foamed Lightweight Soil with High Resistance to Sulphate Erosion Environments.

Foamed lightweight soil (FLS) is a lightweight cementitious material containing a large number of tiny closed pores and has been widely used as a filler in places such as railways, roads and airports. However, there has been little research into the resistance of FLS to sulphate attack in practical engineering applications. The performance of FLS against different sulphate erosion concentrations was studied to elucidate the engineering characteristics of using large volumes of FLS as fill material for the road base in the construction of intelligent networked vehicle test sites. The results showed that the compressive strength of FLS prepared using 30% Portland cement (C), 30% granulated blast furnace slag (GBFS), 40% fly ash (FA) and a small amount of a concrete antiseptic agent (CA) as cementitious materials reached 0.8 and 1.9 MPa at 7 and 28 d, respectively, when the wet density was about 600 kg/m3, which met the design requirements. The FLS prepared via the above-mentioned cementitious system had a low carbon emission, with a CO2 emission reduction rate of up to 70%. It also had excellent sulphate attack resistance: the corrosion resistance coefficient of the cementitious material system reached 0.97, which was considerably better than that of C (0.83). For an erosion medium environment with SO42- concentrations of less than 1000 mg/L (moderate), 40% GBFS or FA can be used to prepare FLS. When the concentration of SO42- is less than 4000 mg/L (severe), 30% C, 30% GBFS and 40% FA can be used as cementitious materials, preferably in combination with an appropriate amount of CA, to prepare FLS.

Machine learning approach to predict the biofuel production via biomass gasification and natural gas integrating to develop a low-carbon and environmental-friendly design: Thermodynamic-conceptual assessment

A hybrid energy cycle (HEC) based on biomass gasification can be suggested as an efficient, modern and low-carbon energy power plant. In the current article, a thermodynamic-conceptual design of a HEC based on biomass and solar energies has been developed in order to generate electric power, heat and hydrogen energy. The planned HEC consists of six main units: two electric energy production units, a heat recovery unit (HRU), a hydrogen energy generation cycle based on water electrolysis, a thermal power generation unit (based on LFR field), and a biofuel production unit (based on biomass gasification process). Conceptual analysis is based on the development of energy, exergy and exergoeconomic assessments. Besides that, the reduction rate of pollutant emission through the planned HEC compared to conventional power plants is presented. In the planned HEC, when hydrogen energy is not needed, excess hydrogen is feed into the combustion chamber to improve system performance and reduce the need for natural gas. Accordingly, the rate of polluting gases emitted from the cycle can be mitigated due to the reduction of fossil fuels consumption. Further, based on the machine learning technique (MLT), the level of biofuel produced from the mentioned process is estimated. In this regard, two algorithms (i.e., Support vector machine and Gaussian process regression) have been employed to develop the prediction model. The findings indicated that the considered HEC can produce about 10.2 MW of electricity, 153 kW of thermal power, and 71.8 kmol/h of hydrogen energy. In both training and testing sets, the Support vector machine model exhibits better behavior compared the two Gaussian process regression model. Based on machine learning technique, with increasing gasification pressure, the level of biofuel obtained from the process does not increase significantly.

Will Mexico meet its climate commitments?

In its Nationally Determined Contribution (NDC), Mexico is committed to reducing unconditionally 22 and 51% of its emissions of greenhouse gases (GHGs) and black carbon, respectively, by 2030, with an emission peak in 2026. Additional reductions of 36 and 70%, respectively, are proposed conditioned to support from other parties. In this work, the percentage of reduction to reach the emission mitigation targets that Mexico proposed in its NDC is estimated. The results show that in order to meet its unconditional NDC, Mexico should start mitigation in 2020 with a 1.5% reduction rate until 2030 and a 3.3% reduction rate by 2050, to reach an emission peak in 2023. To meet the conditional NDC, a 3.1% emission reduction rate until 2030 should be applied, with peak emission in 2021, and 5.8% from 2030 to 2050. In none of these estimates an emission peak in 2026 matches the NDC mitigation options. Furthermore, none of the emissions reduction pathways estimated in this study fulfills the conditional or unconditional contribution and peaks in 2026 at the same time. Mexico has a long history in international climate policy and is a key emerging economy among the top 15 highest GHG emitters. If Mexico does not achieve its NDC, the international implications, both political and climatic, could put the NDC model at risk if there are more large emitters that do not comply with their contribution.

Traffic assignment in urban transportation network problem with emission constraints in China: a cooperative game theory

Traffic assignment in urban transport planning is the process of allocating traffic flows in a network. Traditionally, traffic assignment can reduce travel time or travel costs. As the number of vehicles increases and congestion causes increased emissions, environmental issues in transportation are gaining more and more attention. The main objective of this study is to address the issue of traffic assignment in urban transport networks under an abatement rate constraint. A traffic assignment model based on cooperative game theory is proposed. The influence of vehicle emissions is incorporated into the model. The framework consists of two parts. First, the performance model predicts travel time based on the Wardrop traffic equilibrium principle, which reflects the system travel time. No travelers can experience a lower travel time by unilaterally changing their path. Second, the cooperative game model gives link importance ranking based on the Shapley value, which measures the average marginal utility contribution of links of the network to all possible link coalitions that include the link, and assigns traffic flow based on the average marginal utility contribution of a link with system vehicle emission reduction constraints. The proposed model shows that traffic assignment with emission reduction constraints allows more vehicles in the network with an emission reduction rate of 20% than traditional models.

Capacity optimization of hybrid renewable energy system considering part-load ratio and resource endowment

To help decarbonize the energy sector, a hybrid renewable energy system consisting of wind, solar, and biogas-fueled combined cooling, heating, and power components was investigated. In such systems, thermal and electrical energy are inextricably linked, the local resource endowment is critical, and the main components can be run at part-load. Taken together, these factors make it difficult to optimize a hybrid renewable energy system. To address this issue, we propose a novel method which combines orthogonal design and intelligent algorithms to optimize the capacity of each unit within the system. The optimization results show that the initial investment cost and the dynamic payback period can be reduced by 24.8% and 23.5% compared with optimal orthogonal capacity, and the emission reduction rate increases by 2.3%. In addition, by considering part-load operation and the local resource endowment, it is possible to achieve a 52.2% increase in comprehensive value of the whole system and a 40.4% reduction in investment cost as compared to a simplified model. Importantly, a regional analysis justifies that the proposed evaluation method provides a versatile assessment tool for system optimization.

Desiccant wheel air-conditioning system driven by gas engine cogeneration of heat and power: simulation and analysis by Dymola

Based on the combination of gas engine cogeneration of heat and power (GECHP) technology and temperature and humidity independent control air-conditioning technology, an air-conditioning system combined with desiccant wheel driven by GECHP was proposed to decrease cooling energy consumption and utilize the waste heat of gas engine. In this study, Dymola software was adopted to model the hybrid system and conduct hourly simulation throughout the cooling and heating season for two types of large public buildings: the supermarket building and the office building. Results show that the hybrid system applied to target buildings can meet the requirements of each index of air-conditioning energy supply. Comparing two types of application scenarios, the system primary energy conversion ratio of the supermarket building reaches 112%, 19% higher than that of the office building. And the system CO2 emission reduction rate per unit of capacity of the supermarket building is 0.18 kg/kWh, lower than the 0.22 kg/kWh of the office building. Therefore, the hybrid system has superior energy-saving and environmental performance and is especially suitable for high-temperature and high-humidity areas in summer as well as building application scenarios with high indoor moisture gain and fresh air demand.

Equilibrium pricing mechanism of low-carbon supply chain considering carbon cap-and-trade policy

In the low-carbon supply chain (LSC) network, because of the different carbon emission reduction investment costs and product exhibition costs, members of the same level will show certain competitive behavior. This paper aims to realize the low-carbon development of the supply chain network based on the carbon quota trading system and the risk aversion characteristics of retailers and uses variational inequality theory to construct the network equilibrium model under the constraint of inventory capacity, and designs an improved projection contraction algorithm to solve the model. The results mean that when the retailer is risk averse, the improvement of the risk aversion coefficient (RAC) reduces the manufacturer's carbon emission rate and profit and increases the product sales price and retailer's profit. When the retailer has an inventory capacity constraint, the enhancement of the inventory capacity constraint increases the profit of the manufacturer and the retailer but reduces the emission reduction rate of the manufacturer. If the retailer has risk aversion characteristics and inventory capacity constraints, the manufacturers' profits will increase as RAC increases, while retailers' profits will decrease as RAC increases. In addition, the carbon trading mechanism can promote enterprises to invest in carbon emission reduction and raise the low-carbon level of products.

Scenario Simulation and Effects Assessment of Co-control on Pollution and Carbon Emission Reduction in Beijing

Focused on the key areas of energy, buildings, industry, and transportation, with 2020 as the base year and 2035 as the target year, we respectively designed the baseline scenario, policy scenario, and enhanced scenario, calculated the emission reduction potential of air pollutants and CO2 of Beijing, and constructed an assessment method of co-control effect gradation index to evaluate the co-control effect of air pollutants and CO2 in the policy scenario and enhanced scenario. The results showed that in the policy scenario and enhanced scenario, the reduction rates of air pollutants emissions will reach 11%-75% and 12%-94%, respectively, and reduction rates of CO2 emissions will reach 41% and 52%, respectively, compared with those from the baseline scenario. Optimizing vehicle structure had the largest contribution to the emission reduction of NOx, VOCs, and CO2, and the emission reduction rates will reach 74%, 80%, and 31% in the policy scenario and 68%, 74%, and 22% in the enhanced scenario, respectively. Replacing coal-fired with clean energy in rural areas had the largest contribution to the emission reduction of SO2; the emission reduction rates will reach 47% and 35% in the policy scenario and enhanced scenario, respectively. Improving the green level of new buildings had the largest contribution to the emission reduction of PM10; the emission reduction rates will reach 79% and 74% in the policy scenario and enhanced scenario, respectively. Optimizing travel structure and promoting green development of digital infrastructure had the best co-control effect. The co-control effect of replacing coal-fired with clean energy in rural areas, optimizing vehicle structure, and promoting green upgrading of the manufacturing industry will be improved to a better status in the enhanced scenario. More attention should be paid to improving the proportion of green trips, implementing the promotion of new energy vehicles, and the green transportation of goods to reduce emissions in the field of transportation. At the same time, with the continuous improvement in electrification level in the end energy consumption structure, the proportion of green electricity should be increased by expanding local renewable energy power production and increasing external green electricity transmission capacity, to enhance the co-control effect of pollution and carbon reduction.

Impact of corporate social responsibility on carbon emission reduction in supply chains

PurposeThis paper aims to introduce corporate social responsibility into the green supply chain and analyse the impact of different decision makers’ decision-making schemes on carbon emission reduction in the supply chain.Design/methodology/approachThis study uses a two-stage low-carbon supply chain composed of a manufacturer and retailer as the research object. It uses the Stackelberg game model to analyse optimal carbon emission reduction and its influence under different decision-making modes.FindingsIncreased consumer green preferences and trust can improve the manufacturing enterprises’ carbon emission reduction rate. The carbon emission reduction rate decreases with increased green innovation costs. When green technology innovation costs remain constant, the greater the market capacity, the higher the carbon emission reduction rate. Market capacity has the most significant impact on the optimal carbon emission reduction rate without considering social responsibility decisions and has the least impact on the optimal carbon emission reduction rate while fully considering the social responsibility decision. To achieve decarbonisation production, the market capacity must be small, and when green innovation costs are high, it is the optimal choice without considering social responsibility. To achieve a higher level of carbon emission reduction, when the market capacity is low and the research and development cost is high or when the market capacity is large, it is the optimal choice.Originality/valueThe results provide scientific policy decisions and management significance for governments and enterprises in low-carbon subsidies and supply chain management. The findings also provide a basis for future theoretical research and enterprise practice.

Techno-economic analysis of a combined heat and power system integrating hybrid photovoltaic-thermal collectors, a Stirling engine and energy storage

This paper presents a comprehensive analysis of the energetic, economic and environmental performance of a micro-combined heat and power (CHP) system that comprises 29.5 m2 of hybrid photovoltaic-thermal (PVT) collectors, a 1-kWe Stirling engine (SE) and energy storage. First, a model for the solar micro-CHP system, which includes a validated transient model for the SE micro-CHP unit, is developed. Parametric analyses are performed throughout a year to evaluate the effects of key component sizes and operating parameters, including collector flow rate, storage tank size, SE micro-CHP flow rate, and battery capacity, on the energetic, economic and environmental performance of the proposed system using real hourly weather data, and thermal and electrical energy demand profiles of a detached house located in London (UK). The optimum component sizes and operating parameters are determined accordingly. The daily and monthly operating characteristics of the system are evaluated, and its annual performance is compared to those of a reference system (gas boiler plus grid electricity), as well as of other alternative solar-CHP systems including a PVT-assisted heat pump system and a standalone PVT system. The results indicate that the installation of such a system can achieve an annual electricity self-sufficiency of 87% and an annual thermal energy demand coverage of 99%, along with annual primary energy savings and carbon emission reduction rate of 35% and 37% relative to the reference system. Over 30 years of operation, the net present value (NPV) of the proposed system is £1990 and the discounted payback period is 28 years. The economics of the proposed system is very sensitive to utility prices, especially the electricity purchase price. Relative to the alternative solar systems, the proposed system offers greater environmental benefits but has a longer payback period. This implies that although the energy saving and emission reduction potential of the proposed system is significant, the initial/capital investment, especially of the SE CHP unit and the PVT collector array, are currently high, so efforts should focus on the cost reduction of these technologies.

Railway Emission Trends in China Based on Fuel Life Cycle Analysis

Railway transportation is one of the main modes of modern transportation. Under the dual constraints of air quality improvement and carbon neutrality achievement, clarifying the emission trend of CO2 and pollutants in railway transportation is of great significance for pollution and carbon reduction in the transport sector. In this study, the CO2 and pollutant emission characteristics of Chinese railways from 2001 to 2018 were analyzed based on the fuel life cycle method. Then, railway emission trends from 2019-2030 were assessed combined with scenario analysis. The results showed that with the advancement of railway electrification, the use of new diesel locomotives, and the continuous upgrading of fuel standards, the total CO2 and pollutant emissions in the fuel life cycle of railway transportation showed an upward and downward trend, respectively. In 2018, the total emissions of CO2, NOx, CO, BC, and SOx from railway transportation were 3780.29×104t, 11.98×104t, 3.94×104t, 0.20×104t, and 3.08×104t, respectively. Accelerating the improvement of power structure and reducing unit energy consumption were the best single control strategies to reduce railway emissions of CO2, SOx, NOx, BC, and CO, respectively. Under the comprehensive scenario of actively responding to railway pollution and carbon reduction, the emission reduction rates of CO2, NOx, CO, BC, and SOx could reach 35%, 37%, 39%, 32%, and 45%, respectively. The stagnation of power structure reform or the railway electrification process will lead to a significant increase in total emissions of railway transportation. Therefore, the pollution and carbon reduction of railway transportation requires continuous attention.

Projections of the Emission Reductions of Carbon Dioxide and Conventional Pollutants in the Major Transport Sectors of the Beijing-Tianjin-Hebei Region, China

Many stakeholders recognize that the transport sector should be a major focus for reducing the emissions of carbon and air pollutants since it is the third largest sector for energy consumption in China. This study analyzed and projected the energy consumption and emissions of CO2 and conventional air pollutants (CO, NOX, SO2, and PM2.5) from four transport sectors (highway, waterway, railway and aviation) based on the LEAP model, compared the emission reduction potentials of different transport sectors under different scenarios in 2020-2060, and finally explored the co-reduction effect for CO2 and the four pollutants under different control measures. The results showed that the CO2 emissions from the transportation sectors in the Beijing-Tianjin-Hebei (BTH) region would increase greatly under the baseline scenario. Estimates indicate that the CO2 emissions of Beijing, Tianjin and Hebei Province would increase by 263.72%, 225.87% and 405.43% in 2060, respectively. Under the comprehensive policy scenario, the emission reductions would be 88.78%, 76.86% and 83.20% respectively, and the maximum emission reduction rate of pollutants is expected to reach 78.73%–99.34%. The sectors with major reduction potentials for CO2 and conventional pollutants are the aviation and road transport sectors, which contribute 38.19%–99.85% of the total, respectively. The co-reduction achieved by optimizing the energy structure in the road transport and aviation sectors would be the best. The results of this study can provide a basis for the formulation of low-carbon reduction strategies for the transport sectors in the BTH region.

Decisions for a Retailer-Led Low-Carbon Supply Chain Considering Altruistic Preference under Carbon Quota Policy

With the release of the national energy-saving emission reduction policy and the improvement of consumers’ awareness of environmental protection, the demand for low-carbon products is growing rapidly. In a retailer-led low-carbon supply chain, the increased cost of carbon emission reduction puts manufacturers at a disadvantage. Under the carbon quota policy, to improve manufacturers’ profits as well as enhance carbon emission reduction, this paper studies the players’ decisions in a low-carbon supply chain consisting of one dominant retailer and one manufacturer. To maintain the supply chain’s stability and sustainability, the dominant retailer tends to employ altruistic preference policies towards the manufacturer. The optimal decision, carbon emission reduction and supply chain profit are compared and analyzed under three decision models: (i) centralized decision, (ii) decentralized decision without altruistic preference and (iii) decentralized decision with altruistic preference. The results indicate that the carbon emission reduction rate, market demand and profit in the centralized model are higher than in the decentralized model. The retailer’s altruistic preference is beneficial to the improvement of carbon emission reduction, market demand and the profit of the manufacturer and the supply chain. Under certain conditions, carbon trading can effectively reduce the cost pressure of manufacturers and improve the level of carbon emission reduction and the overall profit of the supply chain. These results will guide low-carbon supply chain decision-making and provide insight into the research of irrational behaviors in supply chain decision-making under carbon policies.

Effects of Carbon Tax on Urban Carbon Emission Reduction: Evidence in China Environmental Governance.

Carbon tax is an important economic instrument in achieving the goal of carbon emission reduction and sustainable development. This paper investigates the effects of carbon tax on carbon emission reduction in China. First, a non-competitive input-output table for Carbon Emissions of 28 sectors in China after Carbon Tax was established, based on the "2018 China Non-competitive Input-Output Table (42 Sectors)" and the carbon emission data of sectors provided by China Carbon Emission Accounts and Datasets (CEADs). Then, an input-output price model was established to study the changes on product price, GDP, employment, and carbon dioxide emissions of 28 sectors after carbon taxing ranged from 10 to 200. When the carbon tax rate reaches 200 yuan/ton, the inflation rate will be 5.907%, the total GDP will be decreased to 1.910%, the total labor force will be decreased to 1.744%, and the total carbon emission reduction rate will be increased to 8.171%. Results showed that with the increases in carbon tax, the inflation rate was increased, the rate of carbon emission reduction was increased, and the negative effects on GDP and employment were also increased. Suggestions on policy making, such as combination of carbon taxing and carbon trading, dynamic adjustment mechanism, tax neutrality, and forcing active carbon reduction, were proposed to minimize the adverse effect of levying carbon tax. The results from this paper would provide a reference for the policy making on carbon management.

Collaborative optimization method and energy-saving, carbon-abatement potential evaluation for nearly-zero energy community supply system with different scenarios

As a typical representative of a green and low-carbon community, a nearly-zero energy community is the main development direction of future building forms. Therefore, to improve the proportion of renewable energy and reduce primary energy consumption and carbon emissions, this paper constructed a nearly-zero energy community supply system, including the battery, heat storage tank, and cold storage tank. A multi-parameter collaborative optimization model considering system design and operation was established to improve the economy, energy-saving, and carbon-abatement potential of the system. This paper took a nearly-zero energy community to study the influence of different electric vehicle numbers on typical daily energy balance, energy-saving, and carbon-abatement potential of the nearly-zero energy community supply system. The results showed that when the number of electric vehicles increased from 0 to 600, the zero energy potential, primary energy saving rate, and carbon emission reduction rate of the nearly-zero energy community supply system decreased by 6.8%, 2.6%, and 1.8%, respectively. The cost per unit supply area increased by 7.8CNY/m2, and the annual cost saving rate increased by 29.7%. The system's optimization design method and energy-saving and carbon-abatement potential evaluation result provided a valuable reference for the integrated building and transportation sector development.

Effects of emission trading scheme (ETS) on change rate of carbon emission

This paper investigates the effects of Emission Trading Scheme (ETS) adoption on the country-level reduction rate of carbon emission. We first used Environmental Kuznets Curve (EKC) tests to group countries into three categories: inverse U-shaped and gamma-shaped EKC for decoupled countries, and a positive linear EKC for non-decoupled countries. We then examined the effectiveness of ETS adoption. We found ETS was effective for both post-industrial and pre-industrial economies. Compared to countries that have not adopted ETS, the carbon emission reduction (increment) rate of decoupled (non-decoupled) countries that have adopted ETS is faster (slower). Furthermore, ETS adoption significantly reduces overall carbon emissions per capita compared to other global events, such as oil crises. The results imply that a market-based mechanism is an effective strategy for achieving sustainable development, thus, providing insights for policymakers and governments to design effective carbon neutrality policies to achieve sustainable development.

Pricing, carbon emission reduction and recycling decisions in a closed-loop supply chain under uncertain environment

<p style='text-indent:20px;'>This paper studies the pricing and recycling decision problems in a closed-loop supply chain (CLSC) containing a manufacturer, a downstream retailer, and a third-party recycling left. The manufacturer is subjected to the cap-and-trade regulation and determines the wholesale price of products and carbon emission reduction rate. The retailer determines its resale price to meet customer demands. The third-party recycling left determines the collection rate of recycling and remanufacturing used products. The new product demands, total carbon emissions, and recovery of these products are characterized as uncertain variables due to lack of historical data or insufficient data collected for research. By constructing three decentralized game models, we explore the equilibrium solutions under the corresponding decision-making situation and the corresponding analytical solutions. Finally, numerical experiments are performed to show the total profit of supply chain members for each structure and some special insights are drawn.</p>

Economic feasibility and policy incentive analysis of Carbon Capture, Utilization, and Storage (CCUS) in coal-fired power plants based on system dynamics.

Carbon Capture, Utilization, and Storage (CCUS) is an important potential technical way for coal power plants to achieve near-zero carbon emissions with the current energy structure in China being dominated by coal. However, CCUS is still at the early demonstration stage, and there are many uncertainties in the business model and policy incentives that the traditional method can no longer handle. At the same time, few studies have analyzed these issues. Taking coal-fired power plants as the research object, we adopt the system dynamics method (SD) to compare the economic feasibility, stability, and CO2 emission reduction effect of the CCUS in the vertical integration model and the CCUS operator model. Furthermore, this paper also studies the effect of different incentive policies on CCUS. Results show the following: (1) Both models are economically viable. From the stability perspective, the CCUS operator model is superior to the vertical integration model. (2) In terms of the effect of individual policies, the government's preference for subsidies to the coal-fired power plant can significantly reduce CO2 emissions. Increasing the additional government subsidy and carbon tax can accelerate the CCUS system into a relatively stable state, but beyond a certain range, the marginal CO2 capture rate, marginal CO2 emission reduction rate, and marginal profit rate of each department will be significantly reduced. The storage enterprise highly depends on the government subsidy. Low government subsidy allocation, low additional government subsidy, and a low carbon tax will lead to continuous negative profits for the storage enterprise, making the CCUS system unsustainable. (3) The impact of combination policies on CCUS is not simply the sum of the effects of individual policies. Each policy has a counteracting or reinforcing effect on the other.

Environment dominated evaluation modeling and collocation optimization of a distributed energy system based on solar and biomass energy

The distributed energy system (DES) with multi-renewable energy complementary fits the current environmental policies and has the characteristics of energy conservation, emission reduction, and environment-friendly. In this work, the DES based on solar and biomass energy is established to meet the energy demand of 50 households in villages of cold areas. Considering the urgent environmental issue, a multi-dimensional evaluation system dominated by the environment is constructed. The maximization of carbon dioxide emission reduction rate, annual cost saving rate and primary energy saving rate are taken as the objective function. The parameters of the main equipment are as the decision variables, integrated with non-dominated sorted genetic algorithm-Ⅱ, and the multi-objective optimization model is founded. The Entropy weight-TOPSIS method is used to solve the optimal capacity of the proposed system under the optimum operation mode. Under all operation modes, the optimized results are better than before optimization. In the mode of mixed operation, the comprehensive evaluation index reaches the highest value, increasing from 48.66% to 52.30%. And the comprehensive environmental index promoted from 75.90% to 82.53%, which is the best value among all schemes, verifying the reliability of the multi-objective optimization model and the emission reduction potential of the proposed system.