Provincial Emissions Research Articles

Dynamic Multi-Factor Correlation Analysis for Prediction of Provincial Carbon Emissions in China’s Bohai Rim Region

This study presents a dynamic multi-factor correlation analysis method designed to predict provincial carbon dioxide emissions (CDE) within China’s Bohai Rim region, including Tianjin, Hebei, Shandong, and Liaoning. By employing the sliding window technique, dynamic correlation curves are computed between various influencing factors and CDE at different time intervals, thereby facilitating the identification of key feature attributes. A novel metric, the Consistency Index of Influencing Factors (CIIF), is introduced to evaluate the consistency of these factors across regions. Furthermore, the Accurate Predictive Capability Indicator (APCI) is defined to measure the impact of different feature categories on the prediction accuracy. The findings reveal that models relying on a single influencing factor exhibit limited accuracy, whereas combining multiple factors with diverse correlation features significantly improves the prediction accuracy. This study introduces a refined analytical framework and a comprehensive indicator system for CDE prediction. It enhances the understanding of the complex factors that influence CDE and provides a scientific rationale for implementing effective emission reduction strategies.

Representative CO2 emissions pathways for China's provinces toward carbon neutrality under the Paris Agreement's 2 °C target

China has committed to achieving carbon neutrality by 2060. It is essential to develop representative CO2 emissions pathways at the provincial level that align with the national target to facilitate effective policy implementation and scientific research. To address inconsistencies between provincial aggregate emissions and national estimates, this study compares the 2021 CO2 emissions estimates of China's provinces from the bottom‒up emissions factor method and the top‒down atmospheric CO2 concentration inversion method. We find that these methods yield comparable results for the emissions from energy combustion and industrial processes at the provincial level. Based on a review of existing research on CO2 pathways for China's provinces, we propose a set of representative pathways for China's provinces. These pathways align with past practices of allocating national emissions intensity reduction targets to provinces and are consistent with the national Tsinghua‒CMA pathway. The proposed pathways require provinces to sequentially peak their emissions by 2030, followed by rapid emissions reduction. Compared to a reference scenario without the carbon neutrality target, these pathways would incur an estimated cumulative GDP loss of about 1% between 2020 and 2060. However, there are notable regional variations, with some regions in the northwest potentially experiencing higher economic growth due to the availability of high-quality low-carbon resources. We recommend that China enhance provincial-level CO2 emissions accounting by cross-validating bottom‒up and top‒down methods. Additionally, careful consideration should be given to aligning provincial targets with national and global commitments when updating pathways toward carbon neutrality.

Analysis of spatial and temporal evolution and driving factors of carbon emission in Shandong Province: based on the perspective of land use

Land use/cover change is the second major contributor to carbon emissions, following energy emissions. Studying provincial land-use carbon emissions is crucial for achieving the “double carbon” goal. This study selects 16 prefecture-level cities in Shandong Province as the research object. It analyzes the spatial and temporal distribution pattern of carbon emissions in Shandong Province based on land-use data and energy consumption. In terms of net carbon emissions, this study utilizes the standard deviation ellipse and kernel density estimation to analyze net carbon emissions change from the municipal and regional perspectives. In terms of carbon ecological carrying capacity, not only the carbon ecological carrying capacity of forest and grassland was considered, but also the carbon ecological carrying capacity of crops in Shandong Province, which is a large grain province. Using the geographic detector to explore the drivers. Research findings indicate that carbon sources and sinks show a clear spatial and temporal distribution pattern, with the center of gravity of net carbon emissions extending to the northeast. Areas with high carbon ecological carrying capacity have high forest coverage, grassland coverage, and crop yields. Regarding driving factors, the urbanization rate, economic aggregate, and technological progress demonstrate significant explanatory power through single and interaction tests, suggesting that these factors are critical drivers of land-use carbon emissions within Shandong Province. Based on the spatiotemporal pattern analysis of land-use carbon emissions in Shandong Province, each city's growth rate and spatial distribution characteristics can be clarified, providing a scientific basis for the local government to formulate regional and differentiated emission-reduction policies. In addition, by exploring the driving factors of land-use carbon emissions in Shandong Province, the influence level of factors on carbon emissions can be revealed to provide references for formulating regional sustainable development strategies.

China's provincial power decarbonization transition in a carbon neutral vision

The power sector plays a crucial role in achieving the China's carbon neutrality target. However, there remain knowledge gaps regarding provincial emission reduction efforts toward carbon neutrality and the impact of carbon flows embodied in electricity transmissions. This study built a modeling framework, linking a bottom-up multi-regional power system model and Quasi-Input-Output approach, to investigate the provincial-level power system transition. Multiple combined scenarios based on shared socio-economic developments and different carbon mitigation policies were designed for a comprehensive assessment. Results indicates that the transition of the power system by 2060, under various scenarios, necessitates 7–7.8 TW of wind and solar photovoltaic power, 4810–6309 TWh of electricity transmissions, and 140–262 GW of coal power retrofits. Notably, carbon emission flows will accumulatively reach 12.7–18.7 Gt from 2020 to 2060. Inner Mongolia will bear a cumulative of more than 5 Gt of carbon flows from North China. When considering the indirect emissions, load centers such as Shandong will have higher peak emissions, and Shandong will replace Inner Mongolia as the largest emitter province. In addition, provincial emission factors under the influence of complex electricity transmissions and carbon flows are estimated.

New perspective, more rational decoupling: A case study of China

Scholars usually unconsciously employ the Tapio decoupling model with a static perspective and results-oriented philosophy, which often leads to errors. Therefore, we propose an improved Tapio decoupling model that adopts a dynamic perspective and process-oriented philosophy. Taking China, the world's largest carbon emitter, as a case study, we investigate the decoupling of its provincial industrial carbon emissions (ICE) from industrial value-added (IVA) during 2005–2020 using both the conventional and improved decoupling model, followed by a comparative analysis of their results. Our findings are as follows: (1) Both China's ICE and IVA exhibited a general upward trend during the study period, with non-linear annual ICE and IVA variations observed across all provinces. (2) Overall, China's provincial IVA increasingly decoupled from ICE, with some provinces achieving a strong decoupling state during 2015–2020; however, from the long-term perspective spanning the entire study period, most provinces remained in a weak decoupling state. (3) The conventional decoupling model tends to yield overly optimistic results in empirical study of China, with the decoupling indices during the periods of 2005–2010 and 2005–2020 determined by the conventional model even had significant statistical difference between those determined by the improved model. (4) To ensure equity, differentiated carbon reduction policies should be tailored to each province, considering factors such as absolute carbon emissions, short-term decoupling states, and long-term decoupling states. The improved Tapio decoupling model is proposed as a valuable framework for researchers engaged in related studies.

Spatio-temporal Heterogeneity of Factors Influencing Transportation Carbon Emissions in Provinces Along the Belt and Road

The administrative units of 17 provinces （autonomous regions and municipalities directly under the Central Government） along the "Belt and Road" were selected as basic spatial units to calculate the provincial traffic carbon emissions along the "Belt and Road" from 2000 to 2021. On the basis of analyzing the spatial and temporal characteristics of traffic carbon emissions by using the spatial autocorrelation method, the spatial and temporal heterogeneity of influencing factors of traffic carbon emissions was explored by combining a fixed-effect regression model and geographic detector. The results show that： ① The provincial traffic carbon emissions along the "Belt and Road" had significant spatial positive correlation, and the overall trend was upward. Additionally, the cluster evolution of high and low values of traffic carbon emissions presented the characteristics of polarization in space. The high value cluster area was mainly distributed in the open leading area, and the low value cluster area was mainly distributed in the core area of the silk road. ② Opening-up level and vehicle ownership were the positive driving factors of carbon emissions from transportation, whereas energy intensity, transportation structure, industry development scale, and government intervention were the negative driving factors. ③ Energy intensity and transportation structure were the main driving factors for the spatial variation of transportation carbon emissions, and most of them would produce nonlinear enhancement when they were spatially superimposed with other factors, that is, there was strong synergy among driving factors. The results showed that the provincial traffic carbon emissions along the "Belt and Road" were affected by the surrounding areas, the influence degree was increasing, and there was synergy between the key driving factors of traffic carbon emissions. Therefore, it is suggested that the provinces along the "Belt and Road" should fully consider the spatial and temporal heterogeneity of traffic carbon emission influencing factors and formulate differentiated traffic carbon emission reduction policies.

Institutional Performance and Carbon Reduction Effect of High-Quality Development of New Energy: China’s Experience and Policy Implication

Based on the policy text from 1999 to 2022, this paper quantitatively analyzes photovoltaic power, wind power and new energy policies in mainland China by keyword capture and policy strength and establishes a spatial Durbin model to study the carbon reduction effects. The results show the following: (1) The development of new energy is primarily project-based and concentrated in rural areas. (2) Financial support is a double-edged sword. (3) After the implementation of the Renewable Energy Law in 2015, the change trend in policy quantity, total policy intensity, and average policy intensity is generally consistent. (4) The increase in the strength of new energy policies has effectively reduced the intensity of provincial carbon dioxide emissions. (5) New energy policies introduced in a single region are less effective in reducing carbon emissions than joint regional regulation. Therefore, it is necessary to improve the quality and coordination of new energy policies through the effective convergence of policies and regular evaluations to enhance the positive guidance of the policies. Policy measures for new energy are refined in three areas: improving the amount and quality of new energy policies, strengthening new energy policies and establishing cooperation mechanisms for the cross-regional consumption of new energy.

Provincial emission inventory of atmospheric polycyclic aromatic hydrocarbons (PAHs) in China

China, as the largest global emitter of atmospheric polycyclic aromatic hydrocarbons (PAHs), exhibits significant variation in energy structure and technological capabilities among provinces. It is vital to construct a provincial-level emission inventory of PAHs, fully considering these provincial disparities, for assessing the environmental budgets and instituting effective mitigation measures. This study constructed a dataset of provincial PAHs emission factors based on downscaling estimates by incorporating the Human Development Index (HDI). These emission factors were combined with the latest activity rate data to estimate PAHs emissions from individual provinces in China from 2007 to 2017. The national total PAHs emissions in our study were 11.8–12.3% higher than the previous emission inventory results. The validation results indicated that the inventory-based PAHs emissions were significantly correlated with the simulated emissions by an atmospheric box model and watershed model. From 2007 to 2017, nearly three-quarters of provinces declined in PAHs emissions resulting from technological advancements and environmental standard improvements, leading to a 34.4% reduction (from 118.5 to 77.8 Gg) of the national total. The industrial sector was consistently the dominant contributor (51.9–66.4%) with a growing trend in its share. This growth was dominantly contributed by less developed provinces, such as Heilongjiang, Ningxia, and Qinghai, indicating that there was a shift of industrial sectors and associated PAHs emissions from highly concentrated industrial regions to nascent industrial regions. This study provides essential data support for a comprehensive understanding of each province's emission responsibilities regarding PAHs and for assessing the potential risks that PAHs pose to human health and ecosystems in these provinces.

Emissions of volatile organic compounds from Chinese coal-fired power plants: Characteristics, source profile, inventories, and impacts

Volatile organic compounds (VOCs) are key precursors for secondary organic aerosols (SOA) and ozone, imposing severe impacts on human health and environment. Considering the massive coal consumption, coal fired power plants (CFPPs) in China are non-negligible VOCs contributors, whose emission characteristics remain inadequately understood. Here, we investigated emission characteristics of 117 VOCs by field tests in four typical CFPPs, and a latest localized CFPPs source profile was compiled by integrating literature reviews. Then speciated-VOCs emission inventories for 2018–2022 were established based on dynamic emission factors and unit-level activity data. The results suggested that oxygenated VOCs (OVOCs) constituted the dominant group (76.5 %), with propionaldehyde (32.0 %) and formaldehyde (24.5 %) being the predominant species. OVOCs (93.2 %) and aromatics (77.4 %) were identified as the primary contributors to ozone and SOA, respectively. Driven by both the rise in coal consumption and technological advancements, nationwide VOCs emissions decreased from 83,393 t in 2018 to 53,251 t in 2022. Regional disparities and varying rates of decline in provincial emissions were evident, with VOCs emissions predominantly concentrated in northern and eastern provinces. Neimenggu, Shandong, Shanxi, Jiangsu, and Guangdong were identified as the top five provinces with the highest emissions. We believe this study would be conducive to a more comprehensive understanding and effective control of VOCs emissions from CFPPs in China.

Energy intensity convergence among Chinese provinces: a Theil index decomposition analysis

China, the world’s largest carbon emitter, has one of the most stringent provincial emissions reduction programs, incorporated into its Five-Year National Plan to reduce carbon emissions. However, the widening energy intensity gap between provinces poses a great challenge for carbon reduction. In this study, we analyze the convergence of Energy intensity (EIC), i.e., the time-dependent decrease of differences among regional energy intensity over time focusing on a data set of 30 Chinese provinces from 2000 to 2015. Our goal is to identify the provinces that are responsible for the observed divergence in energy intensity and identify the factors causing that divergence in each individual case. The Theil index is used to capture inter-provincial energy inequality. We use the LMDI decomposition analysis to identify the drivers of energy inequality (energy consumption structure, energy efficiency, and industrial structure). The results suggest that reducing energy intensity in Inner Mongolia, Xinjiang, and Hebei is the key to solving China's increasing energy intensity “gap” dilemma. The factors causing the energy intensity divergence in Inner Mongolia and Xinjiang are related to lagging economic growth and low energy efficiency, which impedes carbon emission reductions significantly. The factors causing the divergence of energy intensity in Hebei are rooted in its heavy industrial structure. Our findings are directly applicable to crafting regional energy policy with more targeted and practical emission reduction programs.

Establishment of multi-pollutant emission inventory for coal-fired power plants in Guangdong province: A comprehensive approach to air quality management

Emissions of heavy metals from coal-fired power plants pose significant environmental and health risks. This study focuses on quantifying the point-source emissions of heavy metal elements from coal-fired power plants in Guangdong Province, a major economic region with numerous power plants. A bottom-up approach was used to calculate the emissions of mercury (Hg), arsenic (As), and other elements in 2018, along with scenario assumption analyses. The total emissions of Hg, As, Pb, Cd, Cr, and Ni were estimated at 2305, 7443, 7891, 298, 3039, and 7131 kg in 2018, respectively. In addition, the Pearl River Delta (PRD) region, due to its economic development and demand for electricity, has the highest levels of heavy metals emitted from coal-fired power plants in the region, accounting for 42.17-48.60% of the provincial emissions. Scenario simulations showed that the installation of advanced dust removal methods (e.g., LLT-ESP) significantly reduces heavy metal emissions compared to the baseline. It is crucial to continue investing in advanced dust removal technologies and enforce stricter regulations to further mitigate heavy metal emissions from coal-fired power plants.

Can China Reach Its Carbon Peak Before 2030? An Empirical Study Based on China's Carbon Emission Data

The global surge in the warming trend is predominantly attributed to the substantial release of greenhouse gases, notably carbon dioxide. Despite continuous international efforts to curb these emissions, our country, marked by the largest population and energy consumption, stands as a major contributor to carbon dioxide emissions. Unlike developed nations, we face a more formidable challenge in reducing emissions, demanding intensified endeavors. To address climate change, our nation has set a crucial carbon emission target: achieving a carbon peak before 2030 and attaining carbon neutrality by 2060. The "14th Five-Year Plan" of each province underscores the significance of "carbon peaking and carbon neutrality," tailoring emission reduction policies to individual economic landscapes. Current research inadequacies hinder theoretical guidance needed to meet the 2030 carbon peak. Crafting province-specific emission reduction strategies is thus imperative. This study aims to scrutinize national and regional carbon dioxide emissions in four distinct Chinese regions, utilizing the STIRPAT model to predict emissions from 2020 to 2030 under the "low development and strong low carbon" paradigm. Initial analysis from 2004 to 2019 showed an annual increase in national and provincial carbon dioxide emissions, revealing significant regional differences. Six prominent factors influencing emissions were identified. The STIRPAT model predicted emissions for 2020 to 2030, indicating that achieving carbon peaking before 2030 is unfeasible under the baseline scenario. However, within the "low development and strong low carbon" framework, attaining this goal becomes plausible.

Analysis of the Spatial Correlation Network and Driving Mechanism of China’s Transportation Carbon Emission Intensity

From 2008 to 2021, this study analyzed the spatial correlation characteristics between provincial transportation carbon emission intensity and explored ways to reduce transportation carbon emissions. This study used the modified gravity model, social network analysis (SNA) method, and temporal exponential random graph model (TERGM) to analyze the spatial correlation network evolution characteristics and driving mechanism of China’s transportation carbon emission intensity. This study found that China’s transportation carbon emission intensity and spatial correlation network have unbalanced characteristics. The spatial correlation network of transportation carbon emission intensity revealed that Shanghai, Beijing, Tianjin, Guangdong, Fujian, and other provinces were at the center of the network, with significant intermediary effects. The spatial correlation of transportation carbon emission intensity was divided into four functional plates: “two-way spillover”, “net benefit”, “broker”, and “net spillover”. The “net benefit” plate was mainly located in developed regions, and the “net spillover” plate was primarily located in underdeveloped regions. Endogenous structural and exogenous mechanism variables were the main factors affecting the evolution of the spatial correlation network of provincial transportation carbon emission intensity.

China’s cross-regional carbon emission spillover effects of urbanization and industrial shifting

With rapid urbanization and industrial shifting in China, demographic and industrial relocation leads to cross-regional carbon emission spillovers, which are playing an increasingly important role in regional carbon transfer and growth. However, how urbanization and industrial shifting influence carbon spillover still needs to be clarified. In this study, we use multi-regional input-output (MRIO) and scenario analyses to investigate the carbon spillover effects of urbanization, industrial shifting, and their synergy effect on provincial and sectoral carbon emissions in China during 2012–2017. The results showed that urbanization in China further increased carbon spillover in the provinces that already have a high spillover. Industrial shifting, as represented by cross-regional trade between sectors, drove changes in cross-regional carbon spillovers and significantly increased the spillover from Henan, Guangdong, and Hebei by 57.7, 29.9, and 23.6 Mt, respectively. By contrast, the synergy effect of urbanization and industrial shifting on carbon spillover was smaller than their separate effects, with Shandong having the highest increase about 0.61 Mt. Furthermore, at the sectoral level, the production and distribution of electricity and heat-power (PEH), smelting and processing of metals (SPM), and mining and washing of coal (MWC) sectors transmitted the largest carbon emission spillover effects among regions. Based on these findings, more fine-grained policy suggestions for assigning responsibility for carbon reductions and mitigation measures at the regional and sectoral levels in the context of urbanization and industrial shifting.

Analysis of spatial spillover effects and influencing factors of transportation carbon emission efficiency from a provincial perspective in China.

Globally, the transportation industry has become one of the leading sectors in carbon emission, and all countries are committed to environmental protection and energy conservation while experiencing rapid development. Under China's "dual-carbon" goal, the carbon emission problem hinders the construction of China's green transportation system and affects the high-quality development of transportation, so it is of great significance to study the spatial pattern of carbon emission efficiency in the transportation industry and the factors affecting it. Firstly, this paper measures the carbon emission value of transportation in 30 provinces in China from 2010 to 2020 based on the IPCC method and measures the carbon emission efficiency through the super-efficiency slack-based measurement model. Secondly, spatial autocorrelation analysis was conducted to determine the spatial clustering characteristics of the efficiency values. Finally, two spatial Durbin models are constructed to measure the spatial spillover effects and analyze the short-term immediate effects of each influencing factor on the static model and the long-term effects of the dynamic model considering the time lag of the transportation carbon emission efficiency. The results of the study show that (1) the average value of efficiency in the central and eastern regions is basically higher than 0.5; in the western and northeastern regions, it is basically lower than 0.3.The overall efficiency of carbon emission in the region shows a fluctuating upward trend but with increasing regional differences. (2) The number of regions with positive spatial correlation increased from 21 to 25 during the study period, and the degree of provincial transportation carbon emission efficiency agglomeration increased. (3) Although urbanization and energy intensity have a large detrimental influence on transportation carbon emission efficiency, environmental regulation has a major favorable effects on it both long and short term. Population scale, opening level, and urbanization all have significant spatial spillover effects. Accordingly, relevant policy recommendations are put forward to provide theoretical guidance for promoting the realization of low-carbon transportation.

Spatial-temporal characteristics of methane emission in shandong, China, based on an updated comprehensive methane emission inventory in 2020

As methane (CH4) is a powerful greenhouse gas (GHG) responsible for global warming, a refined CH4 emission inventory is crucial for formulating targeted mitigation policies and achieving carbon neutrality. However, provincial comprehensive CH4 emission inventories are rare due to data deficiency and fail to reflect the spatial and temporal characteristics of CH4 emission. Owing to its large population and rapid economic growth, Shandong Province has become the largest GHG emitter in China. Combining bottom-up and top-down methods, a high-spatial-resolution (0.03° × 0.03°) natural and anthropogenic CH4 emission inventory across Shandong Province in 2020 was established based on detailed activity data and updated source-specific emission factors. The total CH4 emission of Shandong was 1711.31 Gg in 2020, of which anthropogenic sources were responsible for 89.96% emission. The top three contributors, namely energy activities, agricultural activities, and waste treatment accounted for 52.86%, 21.25% and 12.70% emission, respectively, followed by vegetation (5.40%), wetland (4.64%), biomass burning (3.14%). Owing to the emission structure, spatial distribution of CH4 emission varied significantly. Jining was the largest contributor (488.92 Gg; 28.57% provincial CH4 emission) owing to the high coal production, followed by Heze (149.17 Gg; 8.72% provincial CH4 emission) and Dongying (138.28 Gg; 8.08% provincial CH4 emission) owing to coal and oil production. The monthly variation of CH4 emission from different sources are different, and the maximum emissions occurred in August. This study reports a set of methods for creating a CH4 emission inventory in China and provides detailed data for air quality simulations and various emission control policies.

Unintentional emissions of polychlorinated naphthalenes in China: Sources, composition, and historical trends

Polychlorinated naphthalenes (PCNs) are detrimental to human health and the environment. With the commercial production of PCNs banned, unintentional releases have emerged as a significant environmental source. However, relevant information is still scarce. In this study, provincial emissions for eight PCNs homologues from 37 sources in the Chinese mainland during the period of 1960-2019 were estimated based on a source-specific and time-varying emission factor database. The results showed that the total PCNs emissions in 2019 reached 757.0 kg with Hebei ranked at the top among all the provinces and iron & steel industry as the biggest source. Low-chlorinated PCNs comprised 90% of emissions by mass, while highly chlorinated PCNs dominated in terms of toxicity, highlighting divergent priorities for mitigating emissions and safeguarding human health. The emissions showed an overall upward trend from 1960 to 2019 driven by emission increase from iron & steel industry in terms of source, and from North China and East China in terms of geographic area. Per-capita emissions followed an inverted U-shaped environmental Kuznets curve while emission intensities decreased with increasing per-capita Gross Domestic Product (GDP) following a nearly linear pattern when log-transformed.

Environmental regulation and outward foreign direct investment: evidence from Chinese firms’ overseas mergers and acquisitions

ABSTRACT While the literature shows that stricter environmental regulations deter exports and inward foreign direct investment (FDI), few studies examine the impacts on outward FDI, especially for developing countries. In this paper, we analyse this issue in the context of China, which is the largest developing country in terms of FDI. We collect the transaction-level data of all the overseas mergers and acquisitions (M&As) by Chinese firms from 2000 to 2019. For the identification of the causal effect, we exploit the implementation of the Environmental Protection Law in 2015 as a quasi-natural experiment and carry out the difference-in-difference-in-differences analysis (DDD). The results show significant impacts of stricter environmental regulations on outward FDI. When the provincial emission reduction target increases by 10% points, the number of overseas M&A transactions in the high-polluting industries has increased by 32 every year since 2015. Moreover, the investments are more likely towards countries with better environmental protection. This suggests that firms aim at gaining cleaner technologies via M&As rather than transferring pollution abroad.

Spatiotemporal evolution trend and decoupling type identification of transport carbon emissions from economic development in China.

Carbon emissions are a major concern in China, and transportation is an important part of it. In this paper, data on China's 30 provinces' transport carbon emissions from 2005 to 2019 were selected to construct a spatial autocorrelation model and identified the decoupling types, which revealed the relationship between transport carbon emissions and economic development. This study suggests a regulation strategy for provincial transport carbon emissions in China based on the contribution rates of transport carbon emission variables. According to the findings, transport carbon emissions of China indicated a slow rise from 2005 to 2019, the annual growth rate has fluctuated downward, and petroleum products have been the most major source. The geographical correlation of transport carbon emissions has gradually improved, and the transport carbon emission intensity has become more significant. Differences in the transport carbon emission intensity slightly increased, which were significantly regionally correlated. There were seven forms of decoupling between yearly provincial transport carbon emissions and economic development, with weak decoupling accounting for the largest proportion, 45.24%. Decoupling was achieved in 83.33% of the provinces in the period of 2005-2019. As a consequence of factor decomposition, the energy intensity, transport intensity, and economic structure played an overall inhibitory role, while the carbon emission intensity, economic scale, and population played promoting roles. The economic scale was the most important influencing factor.

Bridging national and provincial targets: Quantitative evidence for emissions reduction accountability in Korean provinces

As an international collective action to stop global warming, most nations submitted their Nationally Determined Contributions (NDCs) to the United Nations Framework Convention on Climate Change (UNFCCC). To help ensure consistency and comparability of data between different countries, guidelines are provided by Intergovernmental Panel on Climate Change (IPCC). Likewise, subnations would develop their local strategies to achieve a country's NDC. In South Korea, guidelines have been established to facilitate the consistent development of provincial emissions reduction strategies. But the guidelines lack clarity in terms of institutional elements: i) sectoral coverage, and ii) accounting method of greenhouse gas emissions. The primary goal of this study is to show that provincial strategies significantly depend on those two institutional elements. This study examines two scenario sets: i) including or excluding electricity and industrial sectors, and ii) direct or indirect emissions based on a NDC base storyline. For this purpose, we selected GCAM-Korea, a version of the Global Change Analysis Model specifically designed to work at the provincial level in Korea. Simulation results show that those two institutional elements play important roles in evaluating subnational emissions reduction potential. For example, in Chungcheongnam-do, a province in South Korea, the reduction rate from 2018 to 2030 is 53.9%, including the power and industrial sectors. However, when excluding these two sectors, the reduction rate significantly falls to 10.1%. For Gyeonggi-do, the reduction rate based on direct emissions is 9.3%, but it significantly increases to 28.6% when considering indirect emissions. The absence of well-defined methods may result in provinces adopting ineffective and disparate emissions reduction approaches, thereby jeopardizing the attainment of the national target. Consequently, to avert any failure in meeting the national emissions reduction target, it is crucial to furnish uniform and definitive guidelines to all local governments. This approach will ensure a cohesive effort towards greenhouse gas reduction throughout the country.