Decomposing CO2 emission changes in thermal power sector: A modified production-theoretical approach

Revealing the hidden carbon flows in global industrial Sectors-Based on the perspective of linkage network structure

This paper interprets the implicit carbon flows in global industrial sectors from a network perspective. Using the SNA-IO integrated model, along with cross-border input-output data from Eora26 (2000-2020) and global energy balance data, the implicit carbon emissions of global industrial sectors and their evolution are analyzed. A carbon emission network structure from an industrial chain perspective is proposed. The results indicate that the carbon emissions responsibility of an industry is not only associated with its own energy consumption. It also involves the carbon emissions transfer resulting from the exchange of products and services between upstream and downstream industries. Block model analysis reveals the carbon emission transfer relationships and their interconnections among global industrial sectors, tending towards an industry clustering pattern where "production side" converges with "demand side" coexisting in supply and demand. There are noticeable inequalities in wealth gains and environmental burdens between these blocks. This paper can provide targeted carbon reduction policy recommendations for various industrial sectors to participate in global responsibility allocation and promote the formation of a low-carbon global industrial sector network.

Study on spatiotemporal distribution characteristics and driving factors of carbon emission in Anhui Province

Carbon emission is related to global ecological security, and economic development inevitably leads to an increase in carbon emission. In recent years, as a rapidly developing province in China's economy, Anhui Province has experienced significant differences in the spatiotemporal distribution of carbon emission in different regions due to differences in development foundation, urbanization level, population size, industrial structure, etc., providing representative empirical cases for research. Based on the carbon emission data of Anhui Province before the COVID-19, this study used exploratory spatial data analysis method and Geodetector to analyze the spatial and temporal distribution characteristics and drivers of carbon emission in Anhui Province. The study found that (1) the spatial differentiation and spatial correlation of carbon emission in Anhui Province are significant, At the beginning, it shows the characteristics of "high north and low south" and "high west and low east", and then the "core-edge" structure of carbon emission becomes obvious. Carbon emission hotspot areas increase and then decrease, mainly in Hefei, Fuyang and Chuzhou City, etc. The coldspot areas are mainly located in the southern and western mountainous areas, and the degree of aggregation is decreasing year by year. (3) The level of urbanization, economic development and population size are the main driving factors of the spatial variation of carbon emissions, while the industrial structure has the least influence. And most factors produce nonlinear enhancement when spatially superimposed with other factors. (4) The high value areas of economic development, population density, secondary industry structure, and energy intensity are all at high levels of carbon emission, and a combination of factors leads to the creation of high risk areas for carbon emission. The study provides a basis for reducing carbon emission in the next stage of Anhui Province, focusing on key carbon emission areas, and sustainable development.

Energy-water nexus in low-carbon electric power systems: A simulation-based inexact optimization model

Energy and water resources are closely linked in electric power systems, and the application of low-carbon technologies further affects electricity generation and water consumption in those systems. The holistic optimization of electric power systems, including generation and decarbonization processes, is necessary. Few studies have considered the uncertainty associated with the application of low-carbon technologies in electric power systems optimization from an energy-water nexus perspective. To fill such a gap, this study developed a simulation-based low-carbon energy structure optimization model to address the uncertainty in power systems with low-carbon technologies and generate electricity generation plans. Specifically, LMDI, STIRPAT and grey model were integrated to simulate the carbon emissions from the electric power systems under different socio-economic development levels. Furthermore, a copula-based chance-constrained interval mixed-integer programming model was proposed to quantify the energy-water nexus as the joint violation risk and generate risk-based low-carbon generation schemes. The model was applied to support the management of electric power systems in the Pearl River Delta of China. Results indicate that, the optimized plans could mitigate CO₂ emission by up to 37.93% over 15 years. Under all scenarios, more low-carbon power conversion facilities would be established. The application of carbon capture and storage would increase energy and water consumption by up to [0.24, 7.35] × 10⁶ tce and [0.16, 1.12] × 10⁸ m³, respectively. The optimization of the energy structure based on energy-water joint violation risk could reduce the water utilization rate and the carbon emission rate by up to 0.38 m³/10⁴ kWh and 0.04 ton-CO₂/10⁴ kWh, respectively.

Low-carbon development pathways for provincial-level thermal power plants in China by mid-century

Phasing out thermal power plants is vital to combatting climate change. Less attention has been given to provincial-level thermal power plants, which are implementers of the policy of phasing out backward production capacity. To improve energy efficiency and reduce negative environmental impacts, this study proposes a bottom-up cost-optimal model to explore technology-oriented low-carbon development pathways for China's provincial-level thermal power plants. Taking 16 types of thermal power technologies into consideration, this study investigates the impacts of power demand, policy implementation, and technology maturity on energy consumption, pollutant emissions, and carbon emissions of power plants. The results show that an enhanced policy combined with a reduced thermal power demand would peak carbon emissions of the power industry at approximately 4.1 GtCO₂ in 2023. Meanwhile, most of the inefficient coal-fired power technologies should be eliminated by 2030. Carbon capture and storage technology should be gradually promoted in Xinjiang, Inner Mongolia, Ningxia, and Jilin after 2025. Energy-saving upgrades on 600 MW and 1000 MW ultra-supercritical technologies should be emphatically carried out in Anhui, Guangdong, and Zhejiang. By 2050, all thermal power will come from ultra-supercritical and other advanced technologies.

CO2 emissions in China's power industry by using the LMDI method

WIth the introduction of "carbon peak and neutrality" targets, China's power industry is under enormous pressure to reduce carbon dioxide (CO₂) emissions, as it produces more than 40% of emissions. In response, China's power industry is actively reducing the investment in thermal energy and gradually shifting toward non-fossil energy sources. However, the CO₂ reduction effect of these measures is still unknown. This study aims to analyze CO₂ emissions from China's power industry from 2009 to 2018 from an entire lifecycle perspective, considering that CO₂ emissions also exist in non-fossil power generation. The logarithmic mean Divisia index (LMDI) method is employed to identify the factors influencing CO₂ emissions. Then, the modified STochastic Impacts by Regression on Population, Affluence and Technology model is used for comparative validation. The results show that (1) CO₂ emissions from China's power industry increased significantly, from 276.5 million tons of CO₂ equivalent (Mtce) in 2009 to 436.44 Mtce in 2018; (2) the investment intensity, investment structure, and emission intensity dampen CO₂ emissions, with cumulative contribution rates of - 28.88%, - 11.89%, and - 3.16%, respectively. The investment efficiency, economic development level, and population size contribute to CO₂ emissions, with cumulative contribution rates of 29.76, 24.68, and 1.07%, respectively; and (3) Investment into the hydropower contributes the least to CO₂ emissions, followed by wind, nuclear, photovoltaic, and thermal power. These research findings suggest that the power industry should improve its investment decision-making capabilities and pay particular attention to the hydropower-led non-fossil energy sector.

How can China achieve its goal of peaking carbon emissions at minimal cost? A research perspective from shadow price and optimal allocation of carbon emissions

The Chinese government has declared a determination at the 75th United Nations General Assembly that China will improve its independent contribution and adopt more powerful measures to peak the carbon emissions before 2030. However, such strict implementation of carbon reduction policies is bound to bring the cost of sacrificing economic development. In such a context, this paper tries to use shadow price to measure the average social cost of emission reduction, marginal abatement cost to depict the pressure to reduce carbon emissions based on non-radial distance function, and provides an optimal scheme for provincial emission reduction to minimize the national cost of emission reduction based on variable-coefficient model. Results show that: First, the average value of shadow price is 15.91 and varies widely among regions, which means on average reducing one unit of carbon emissions will sacrifice 15.914 yuan RMB of economic output, and there exists possibility of carbon transactions across regions; Second, on the one hand, marginal abatement cost of carbon emission for most regions presents an upward tendency over time, which means greater economic costs have to be sacrificed with economic development in the future; On the other hand, marginal abatement cost is much higher in regions with high economic level than that in the economically undeveloped areas, which indicates reducing carbon emissions is becoming increasingly difficult and would pay more economical cost in economically developed regions; Third, the optional allocation scheme of CO2 reduction derived from this research is better than administrative ways of Grandfathering and Benchmarking in terms of minimizing emission reduction cost. Results of this paper indicate that larger carbon trading market can be implemented in China to economically fulfill the commitment of peaking carbon emissions.

What drives energy consumption in Indonesia’s manufacturing industry? An analysis of firm-level characteristics

Purpose

This study aims to identify and analyse energy intensity in Indonesia’s manufacturing industry based on industrial sub-sector, island region, technology intensity, firm size, type of ownership and exporter status to determine which of these characteristics have the highest potential to decrease energy intensity.

Design/methodology/approach

Using firm characteristics data from statistics of large and medium industries in Indonesia, this study decomposed energy consumption of Indonesian firms into economic activity, economic structure and energy intensity for the period 2010–2014 through the logarithmic mean Divisia index (LMDI).

Findings

The results showed the decomposed energy intensity based on the six sub-categories. From the sub-categories, several characteristics which induced the most increases in energy intensity are highlighted. Several industrial sub-sectors were classified as highly energy-consuming, including rubber and plastic products, glass and non-metal mineral products, food, electrical machinery and apparatus, chemical, paper, motor vehicles and trailers and tobacco. Results from other sub-categories indicated that firms with high energy intensity were located in the Java--Bali region, had medium technology intensity and were exporters. Meanwhile, firm size and ownership type sub-categories did not show clear differences in energy intensity.

Practical implications

This study provides more focused policy recommendations for related policymakers and stakeholders to emphasise the most energy-inefficient and energy-intensive firm based on the results from each sub-category and hence policy priorities to reduce energy consumption can be well-targeted.

Originality/value

This study contributes to the field through a more thorough energy intensity analysis based on the classification of Indonesian firm characteristics to provide a more detailed insight on the cause of the ever-increasing energy intensity level in the country.

Decomposition analysis of industrial pollutant emissions in cities of Jiangsu based on the LMDI method

Cities are faced with various kinds of pollution issues in the process of economic development, among which industrial pollution has become the most terrifying environmental issue in recent years, so that industrial pollution control should be emphasized. Finding out the key factors influencing industrial pollutant emissions is the basis of taking corresponding measures. Previous studies only focused on one pollutant without a comparative analysis of the contribution of influencing factors to multiple pollutants. Therefore, this study aims to identify the key influencing factors of industrial pollutants in Nanjing, Suzhou, Xuzhou, and Taizhou in Jiangsu Province during the years 2008-2018 by using the logarithmic mean Divisia index (LMDI) method. The results from decomposition indicate the following. (1) Emission intensity (EI) and energy efficiency (EE) are negative factors for decreasing industrial pollutant emissions, while the economic output (EO) and population (P) are positive factors for increasing industrial pollutant emissions. (2) Emission intensity has the most significant influence to industrial wastewater in decreasing emissions; energy efficiency makes the biggest contribution to industrial solid waste in decreasing emissions, economic output and population contribute the most to industrial solid waste in increasing emissions. (3) Nanjing has the highest contribution rate of emission intensity and population, and the contribution rate of energy efficiency and economic output to Taizhou is the highest. Identifying the key driving factors of different pollutants can serve as evidence and guidance for urban environmental governance, therefore reducing emissions ulteriorly, and achieving sustainable development.

The Prediction of Carbon Emission Information in Yangtze River Economic Zone by Deep Learning

This study aimed to respond to the national “carbon peak” mid-and long-term policy plan, comprehensively promote energy conservation and emission reduction, and accurately manage and predict carbon emissions. Firstly, the proposed method analyzes the Yangtze River Economic Belt as well as its “carbon peak” and carbon emissions. Secondly, a support vector regression (SVR) machine prediction model is proposed for the carbon emission information prediction of the Yangtze River Economic Zone. This experiment uses a long short-term memory neural network (LSTM) to train the model and realize the experiment’s prediction of carbon emissions. Finally, this study obtained the fitting results of the prediction model and the training model, as well as the prediction results of the prediction model. Information indicators such as the scale of industry investment, labor efficiency output, and carbon emission intensity that affect carbon emissions in the “Yangtze River Economic Belt” basin can be used to accurately predict the carbon emissions information under this model. Therefore, the experiment shows that the SVR model for solving complex nonlinear problems can achieve a relatively excellent prediction effect under the training of LSTM. The deep learning model adopted herein realized the accurate prediction of carbon emission information in the Yangtze River Economic Zone and expanded the application space of deep learning. It provides a reference for the model in related fields of carbon emission information prediction, which has certain reference significance.