Predicting low carbon pathways on the township level in China: a case study of an island

Carbon prediction on the township level is usually difficult due to a lack of necessary information. To fulfil the research gap, the study focused on a town located in a nearshore island (Lingshan) in China. A questionnaire survey was performed to collect essential information about the future development of the town, followed by validating interviews with the island management committee. The carbon prediction of the town was established by the Low Emissions Analysis Platform (LEAP) model. The baseline scenario reflecting the existing method of carbon management was compared with an alternative low-carbon scenario. The prediction from 2020 to 2060 covers the periods of the planned carbon emissions peak in 2030 and carbon neutrality in 2060. It is found that energy-related activities and electricity consumption are the primary contributors to carbon emissions on the island. The carbon emission of Lingshan Island increases from -1333 tCO2e in 2020 to 2744 tCO2e in 2060, and the carbon peak target cannot be achieved in the baseline scenario. However, the carbon emission of the low-carbon scenario is predicted to have a peak of -850 tCO2e in 2029. The prediction model developed in this study, along with the proposed policy recommendations, can be applied to other towns or regions where data availability is limited to promote carbon reduction.

Pathways for regions to achieve carbon emission peak: New insights from the four economic growth poles in China

Regional synergy is critical to achieving High Quality Development (HQD) and reducing emissions in China. Economic growth poles (EGPS), namely Beijing-Tianjin-Hebei, the Yangtze River Delta, Guangdong-Hong Kong-Macao, and Cheng-Yu, are typical examples of regional synergy in China. It is critical to explore whether the pulling power of the EGPS to other regions can accelerate China's carbon peaking. First, this study applies the Miller-Round model to measure the spillover effects of the EGPS and selects the radiation-driven areas. Second, based on the environmental Kuznets curve hypothesis, a panel smoothing transformation model is applied to explore the relationship between regional HQD and carbon emissions. Finally, under different scenarios, the inter-regional spillover effect is used to explore the path to achieving the carbon emissions peak. The results show an inverted U-shaped relationship between carbon emissions and HQD. Additionally, with the spillover pull of the EGPS, the peak carbon emission time of all provinces is earlier by 1-6 years in different scenarios, and it can promote Ningxia, Qinghai, Gansu, Guizhou to achieve a carbon peak by 2030. However, the pulling effects of Shanxi, Shaanxi, Jilin, and Guangxi require further improvement. Therefore, the policy implications of increasing inter-regional production efficiency, improving innovation levels, and using renewable energy are proposed to improve the level of HQD, thus achieving a carbon peak. Moreover, improving the industrial linkage between the EGPS and other regions would also be effective. The industrial structure promotes the cultivation of the EGPS in Cheng-Yu and strengthens regional integration in the western region.

Potential pathways to reach energy-related CO2 emission peak in China: analysis of different scenarios

The prevalence of global unilateralism and the shock of COVID-19 brought considerable uncertainty to China's economic development. Consequently, policy selection related to the economy, industry, and technology is expected to significantly impact China's national economic potential and carbon emission mitigation. This study used a bottom-up energy model to assess the future energy consumption and CO₂ emission trend before 2035 under three scenarios: a high-investment scenario (HIS), a medium-growth scenario (MGS), and an innovation-driven scenario (IDS). These were also used to predict the energy consumption and CO₂ emission trend for the final sectors and calculate each sector's mitigation contribution. The main findings were as follows. Firstly, under HIS, China would achieve its carbon peak in 2030, with 12.0 Gt CO₂. Moderately lowering the economic growth rate to support the low-carbon transition of the economy by boosting the development of the low-carbon industry and speeding up the employment of key low-carbon technologies to improve energy efficiency and optimize energy structure in the final sectors, the MGS and the IDS would achieve carbon peak approximately in 2025, with a peak of 10.7 Gt CO₂ for the MGS and 10.0 Gt CO₂ for the IDS. Several policy recommendations were proposed to meet China's nationally determined contribution targets: instigating more active development goals for each sector to implement the "1+N" policy system, taking measures to accelerate the R&D, boosting the innovation and application of key low-carbon technologies, strengthening economic incentives, forming an endogenous driving force for market-oriented emission reduction, and assessing the climate impacts of new infrastructure projects.

Regional allocation of carbon emission quotas in China under the total control target

The allocation of provincial carbon emission quotas under total amount control is an effective way for China to achieve its carbon peak and neutrality targets. Firstly, in order to study the factors influencing China's carbon emissions, the expanded STIRPAT model was constructed; and combined with the scenario analysis method, the total of national carbon emission quota under the peak scenario was predicted. Then, the index system of regional carbon quota allocation is constructed based on the principles of equity, efficiency, feasibility, and sustainability; and the allocation weight is determined by the grey correlation analysis method. Finally, the total carbon emission quota under the peak scenario is distributed in 30 provinces of China, and the future carbon emission space is also analyzed. The results show that: (1) only under the low-carbon development scenario, can China reach the peak target by 2030, with a peak carbon of about 14,080.31 million tons; (2) under the comprehensive allocation principle, China's provincial carbon quota allocation is characterized by high levels in the west and low in the east. Among them, Shanghai and Jiangsu receive fewer quotas, while Yunnan, Guangxi, and Guizhou receive more; and (3) the future carbon emission space for the entire country is modestly surplus, with regional variations. Whereas Hainan, Yunnan, and Guangxi have surpluses, Shandong, Inner Mongolia, and Liaoning have significant deficits.

How to advance China's carbon emission peak?- A comparative analysis of energy transition in China and the USA

This paper investigates carbon emission peak in China based on a comparative analysis of energy transition in China and the United States (US). The LMDI model is adopted to decompose carbon emissions into several driving factors in 2000-2018 for China and the US. Gray forecasting and NAR neural network are combined to predict peak time and identify optimal transition paths. The factor decomposition indicates that energy intensity is the main inhibitory factor for increased carbon emissions, while economic growth and population size are contributors for increased carbon emissions. There are significant differences in the impact of structure effect on carbon emissions in the two countries. The industry decomposition indicates that industry development is a critical inhibitor for increased carbon emissions after 2014 in China. The growth of transport and agriculture are basically contributing to increase carbon emissions in China and the US. The forecast results illustrate that China could complete carbon emission peak by 2030 under the baseline scenario, with a peak volume of 11354.72Mt CO₂. Under the industrial structure adjustment scenario, the carbon peak year may be advanced to 2028. While adjusting industrial structure and energy consumption structure at the same time, China could achieve carbon emission peak at 9918.21Mt CO₂ in 2025.

Synergistic effect of carbon ETS and carbon tax under China's peak emission target: A dynamic CGE analysis

Global warming resulting from greenhouse gas emissions poses threats to humankind and has become a worldwide issue. As the top CO₂ emitter in the world, China has committed to achieving its carbon emission peak by no later than 2030; in this context, how to best use and apply carbon emission reduction policy is particularly critical. By constructing a dynamic computable general equilibrium (CGE) model, we first examine a pure ETS included only the electricity sector in 2021, and the eight sectors starting in 2022, considering a declining carbon intensity rate of 4.5% and a higher rate of 4.8%. With the carbon intensity rates of 4.3% and 4.5%, we further evaluate two-hybrid systems of the carbon tax and carbon ETS, where the carbon tax of 10 yuan per ton is the starting levied rate in 2022 and increases at 4 yuan per ton year by year. The results proved that hybrid emission reduction policy can help reach a carbon emissions peak before 2030 and do so at a lower economic cost compared to the effect of pure carbon ETS. Besides, the coordinated use of a carbon tax and a carbon ETS can promote optimization of energy consumption structures and accelerate the decline of energy intensity and carbon intensity; this can contribute to curbing the growth of total energy consumption and total carbon emissions.

Consumption-based CO2 emissions accounting and scenario simulation in Asia and the Pacific region

This study uses a consumer-based accounting approach to evaluate the CO₂ emission factors of 17 countries in Asia and the Pacific region by including all emissions in the supply chain from commodity location to final consumption location in country consumption patterns. In addition, the number of emissions connected with each country's consumption of products and services in Asia and the Pacific region has received little attention. This study contributes to understanding the effects of countries' consumption of products and services on carbon emission peaks and formulates efficient carbon mitigation plans for governments and decision-makers. Accelerating economic growth and industrialization have posed significant challenges to global carbon mitigation efforts and climate change responses. The Monte Carlo simulation technique was used to create a dynamic scenario simulation model to investigate possible future peaks in the carbon emissions of countries in Asia and the Pacific region while taking into account factor uncertainties. The results show that total consumption-based CO₂ emissions are remarkable in three Asian countries, including China (387,451.95 metric tons Mt CO₂), Japan (185,259.60 Mt CO₂), and India (100,720.46 Mt CO₂). In South Korea, Brunei, and Taiwan, annual consumption emissions are 1.77, 1.62, and 1.49 tons of CO₂ per person, respectively. In terms of final consumption, the household sector is the most noteworthy contributor to consumption-based emissions, accounting for 27-56%. The household sector probably peaks at 19.7 Gt CO₂ as per the dynamic scenario simulation. For the three other types of final demand, government expenditure will possibly reach a maximum of 44.0 Gt CO₂ in the next 30 years, while capital formation will probably reach its highest emission level at 149.5 Gt CO₂.

Carbon Kuznets curve in China's building operations: Retrospective and prospective trajectories

China has pledged to achieve peak carbon emissions by 2030 and to be carbon neutral by the mid-century, and buildings will be the "last mile" sector in the transition to carbon neutrality. To help buildings hit the carbon peak goal, we investigate the different emission scales of carbon emission changes of residential and commercial building operations across 30 provinces in China through the carbon Kuznets curve (CKC) model. We observe that (1) more than three-quarters of the samples can be fitted by the CKC model. Most CKCs are the inverted U-shaped, residential and commercial buildings occupying 93% and 90% at the total emission scale, respectively. In addition, the remains can be illustrated as N-shaped curves. (2) Under the premise of CKCs existence, approximately half of the provincial residential and commercial buildings peak at different emission scales, except for emission per floor space (residential: 89%; commercial: 81%). Provinces with better economic development have a higher peaking probability. In the total emissions, the peaking probability in residential buildings is 33% and 50% for provinces with economic indicators <20,000 Chinese Yuan and 30,000-40,000 Chinese Yuan, respectively, and 22% and 67% for commercial buildings, respectively. (3) Taking carbon intensity as a case study, decoupling analysis examines the robustness of the CKC estimation. Overall, we close the gap of the CKC estimation in commercial and residential buildings, and the proposed methods can be treated as a tool for other economies to illustrate the retrospective and prospective trajectories of carbon emissions in building operations.

Co-benefits of peaking carbon dioxide emissions on air quality and health, a case of Guangzhou, China

Cities play a key role in making carbon emission reduction targets achievable and tackling air pollution. Using Guangzhou city as a case, this paper explored the air quality and health co-benefits of peaking carbon dioxide emissions under three scenarios and developed an integrated assessment framework by combining a local air pollutant emission inventory, an atmospheric chemistry transport model, and a health assessment model. The results showed that SO₂, PM₁₀, and PM_2.5 could achieve larger emission reductions than NH₃, VOCs, and NOx among all the scenarios we examined. Under the enhanced peaking scenario with the most stringent mitigation strategies, Guangzhou could meet the local ambient air quality standard for PM_2.5 (34 μg/m³), with the most reduction observed in the annual average PM_2.5 concentration (28.4%) and related premature deaths (17.08%), compared with the base year 2015. We also identified hotspot grids, which were areas with high concentrations of carbon emissions, high concentrations of air pollution and poor air quality in Guangzhou. Our analysis highlighted the importance of promoting peaking carbon dioxide emission for the improvement of air quality and public health at the city level.

Analysis on the carbon emission peaks of China's industrial, building, transport, and agricultural sectors

Carbon emission peak has become a focus of political and academic concern in global community since the launch of Kyoto Protocol. China, as the largest carbon emitter, has committed to reaching the carbon peak by 2030 in Paris Agreement. This ambitious national goal requires the endeavors of individual sectors, particularly those carbon-intensive ones. Predicting the sectoral peaks under current endeavors and understanding driving forces for the carbon emission changes in the past years are substantial for guiding the allocation of the country's future efforts. In the past studies contextualized in China, the prediction of its carbon peaks seldom appeared at the sectoral level, which is considered as a research gap. Therefore, this study predicts the peaks at four carbon pillar sectors (i.e. industrial, building, transport and agricultural sectors) and identifies the driving forces for the carbon emission changes of them. This study hypothesized Carbon Kuznets curve (CKC) as the theoretical model for predicting the peaks and used Logarithmic mean Divisia index (LMDI) as the method to identify the driving forces. The results show that the carbon emission in the country will peak in 2036, six years later than the agreed year. The lateness of the national peak can be attributed to the significant lateness of three pillar sectors' peaks, occurring in 2031 for the industrial sector, 2035 for the building sector, 2043 for the transport sector, peak for the agricultural sector occurs four years earlier in 2026 though. Furthermore, the results show that carbon emission is significantly driven by the booming economic output and inhibited by decreasing energy intensity, but the slight fluctuation of energy structure plays a minor role in the four sectors. Policy adjustments are proposed for effectively and efficiently urging the on-time occurrence of the national peak.

The semi-centennial timescale dynamic assessment on carbon emission trajectory determinants for Hebei Province within the New Normal pattern shock

New normal development pattern has already been experiencing in China since the rate of economics slowing down recent years. This new development circumstance requires targeted and adapted policy instruments as well as mitigation measures to facilitate to achieve carbon emission peak around 2030. In the context of the new normal pattern, its advancement effectiveness that boosting on carbon emission trajectory is calculated by three combination models over a semi-centennial time scales ranging from 1985 to 2035 in this study. Hebei province is estimated as the empirical case for exploring the concrete response due to its critical status. Carbon emission trajectory is revealed from both historical and future perspectives. Historical trajectory reflects the changing trend of carbon emission over time spans since 1985 and before which new normal pattern occurred. On the converse, future trajectory projects the vary orientation of carbon emission between the period around new normal occurred and up to 2035. The carbon emission trajectory is separated into historical trajectory and future trajectory taking the initial time of the New Normal period as the dividing boundary. The results show that, the peaking time for Hebei province would be appeared at 2022, 2024, and 2026 with the peaking level of 226.78, 238.22, and 250.95 million tons, respectively. A lower increasing rate of 7% for GDP, a gradually decreasing proportion of the secondary industry ranging from 44.99% by 2020 to 37.9% by 2030, and a moderate growing magnitude for energy consumption restrained beyond 371.15 Mtce towards 2030 is identified as the optimal pathway for reaching carbon emission peak.

Feasibility of peaking carbon emissions of the power sector in China's eight regions: decomposition, decoupling, and prediction analysis

Carbon emissions in the power sector are an important part of China's total carbon emissions and have a significant impact on whether China can achieve the 2030 carbon peak target. Based on the three perspectives of decomposition, decoupling, and prediction, this paper studies the feasibility of carbon emission peaks in eight major regional power sectors in China. First, the generalized Divisia index model (GDIM) is used to decompose the carbon emissions of the eight regional power sectors, and the driving factors and their effects on carbon emissions in the power sector of each region are compared. Then, the decoupling index based on the generalized Divisia index model (GDIM-D) is used to study the decoupling relationship between the carbon emissions of the eight regional power sectors and economic growth. Finally, the carbon emissions and decoupling indices of the power sector from 2017 to 2030 are predicted. The results show the following. First, the gross domestic product (GDP) and output scale are the main factors contributing to the carbon emissions of the eight regional power sectors. The carbon intensity of the power sector in GDP (C/G) and output carbon intensity(C/E) are the main factors that contribute to the reduction. Second, the carbon emissions of the southern coast, the middle Yellow River, and the Southwest peaked in 2013 and have been decoupled from economic growth, while those in the other regions have not peaked or decoupled. Third, if the carbon emissions of the power sector in the Northeast, northern coast, eastern coast, middle Yangtze River, and Northwest reach a peak in 2030, they will face many emission reduction pressures. This paper provides a reference for studying the carbon emissions of China's regional power sectors and their relationship with economic growth and has important implications for peak carbon emissions at the national level.