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With the continuous expansion of regional trade, interregional teleconnections have an increasingly pronounced impact on the changes in carbon storage caused by land use in China. Current research in this area remains remarkably limited, however. This study employs land-use data, carbon density data, and a multiregional input–output model to precisely quantify the embodied flow and spatial patterns of land-use carbon storage in China. We found that the carbon storage embodied in interprovincial trade in China accounts for 34.9 percent of total carbon storage, with cropland (44.9 percent) and woodland (43.6 percent) representing the highest proportions within their land-use categories. Inner Mongolia, Heilongjiang, and Tibet were found to be the most externally driven in terms of land-use-related carbon storage, with outflows exceeding 2,000 Tg C. Beijing, Tianjin, and Shanghai, on the other hand, were shown to consume up to 90 percent of land-use-related carbon storage inflows, showcasing the strongest external dependence. Additionally, Hunan (1,300.5 Tg C), Hubei (863.4 Tg C), Henan (1,534.6 Tg C), and Shanxi (2,223.6 Tg C) are the provinces where changes in land-use carbon storage are most significant, exerting the greatest influence on changes in China's total carbon storage. China's international export of land-use-related embodied carbon storage decreased by 3,964.7 Tg C during the study period, with woodland (1,516.8 Tg C) and grassland (1,505.7 Tg C) being the primary declining land types. Interestingly, as land utilization efficiency continues to rise, over half of the provinces have seen an increase in export trade value, while their corresponding embodied carbon storage has declined. The findings not only unveil the complex dynamics and spatial distribution at work in China's embodied land-use-related carbon storage but also provide practical insights and directions for achieving carbon neutrality goals. [ABSTRACT FROM AUTHOR]

This paper explores the socioeconomic determinants of CO2 emissions in China from a historical and prospective perspective. Through the study, we developed a comprehensive analysis framework, using time series data on China’s economic structure, energy consumption structure, income, urbanization, FDI, and total trade for the period 1980–2014 in order to perform our analysis. Firstly, the results of ADF test indicated that the surveyed variables were stationary and integrated of I(1), a finding that was further confirmed by the results of a KPSS test. Subsequent Johansen cointegration results found that there existed no less than one cointegrating relationship between the surveyed variables, and the results of a VECM model suggested that all of the selected socioeconomic factors exerted important influences in determining the value of the dependent variable, CO 2 emissions. The Granger causality tests demonstrated the existence of a bidirectional causal link between CO 2 emissions and the economic structure, and between CO 2 emissions and the energy consumption structure. A unidirectional causal link was also located that runs from CO 2 emissions to GDP, urbanization to CO 2 emissions, and CO 2 emissions to trade. No causal link was found to exist between CO 2 emissions and FDI. In addition, impulse response functions and variance decomposition analysis revealed that the energy consumption and economic structures can be expected to continue to exert strong and significant forecasted impacts on CO 2 emissions in the future, while the impact of FDI and total trade is expected to be of increasingly limited magnitude. The impact of GDP is also likely to be evident in the future. These results hold important implications for governmental policy decisions pertaining to the reduction of China’s CO 2 emissions. [ABSTRACT FROM AUTHOR]

We examine carbon trade imbalances among 172 countries over a 10-year period (2000–2010). A carbon trade imbalance refers to the extent to which the carbon emissions embodied in a country's exports exceeds the emissions embodied in its imports. Although past research has considered how such imbalances coincide with variances in wealth and/or trade patterns, none have considered how such imbalances arise in the context of an evolving network structure. In this paper, we consider trade networks and carbon trade imbalances as co-evolving phenomena, and study these using an innovative combination of multi-regional input–output (MRIO) analysis with stochastic actor-oriented models (SAOMs). Our findings both challenge and support arguments made by ecological unequal exchange theory and the Gravity Model, and highlight the role emerging economies play in shaping network structure and the distribution of carbon trade balances overtime. [ABSTRACT FROM AUTHOR]

Nearly three-quarters of the growth in global carbon emissions from the burning of fossil fuels and cement production between 2010 and 2012 occurred in China. Yet estimates of Chinese emissions remain subject to large uncertainty; inventories of China's total fossil fuel carbon emissions in 2008 differ by 0.3 gigatonnes of carbon, or 15 per cent. The primary sources of this uncertainty are conflicting estimates of energy consumption and emission factors, the latter being uncertain because of very few actual measurements representative of the mix of Chinese fuels. Here we re-evaluate China's carbon emissions using updated and harmonized energy consumption and clinker production data and two new and comprehensive sets of measured emission factors for Chinese coal. We find that total energy consumption in China was 10 per cent higher in 2000-2012 than the value reported by China's national statistics, that emission factors for Chinese coal are on average 40 per cent lower than the default values recommended by the Intergovernmental Panel on Climate Change, and that emissions from China's cement production are 45 per cent less than recent estimates. Altogether, our revised estimate of China's CO2 emissions from fossil fuel combustion and cement production is 2.49 gigatonnes of carbon (2 standard deviations = ±7.3 per cent) in 2013, which is 14 per cent lower than the emissions reported by other prominent inventories. Over the full period 2000 to 2013, our revised estimates are 2.9 gigatonnes of carbon less than previous estimates of China's cumulative carbon emissions. Our findings suggest that overestimation of China's emissions in 2000-2013 may be larger than China's estimated total forest sink in 1990-2007 (2.66 gigatonnes of carbon) or China's land carbon sink in 2000-2009 (2.6 gigatonnes of carbon). [ABSTRACT FROM AUTHOR]

The number of energy–water nexus studies has been increasing recently due to the significant linkages between energy generation and water consumption, but no study has looked at water quantity and quality impacts as well as carbon emissions associated with electricity production. Using integrated hybrid life cycle analysis, this study examines the life cycle impacts of pulverized coal, wind power and solar power on carbon dioxide (CO 2 ) emissions, water consumption and water quality in Inner Mongolia, China. Our research findings show that pulverized coal emits 1213.5 g of CO 2 per kilowatt-hour (g/kW h) of electricity output, compared with 34.4 g/kW h for wind power and 67.4 g/kW h for solar photovoltaic. Water consumption for pulverized coal, wind power and solar photovoltaic are 3.3, 0.7 and 0.9 l/kW h, respectively. The water requirement to dilute the life cycle chemical oxygen demand (COD) discharge would increase water consumption during production processes of pulverized coal, wind power and solar photovoltaic systems by 0.11, 0.09 and 0.19 l/kW h, respectively. Given that the State Grid Corporation of China aims to increase the power generation capacity that provides power supply to regions outside Inner Mongolia to 120 GW by 2020, electricity outflows could contribute 520 million tonnes of CO 2 emissions, which would be similar to the CO 2 emissions of the UK in 2010, and 1460.8 million m 3 of water. This study reveals that substantial reductions in CO 2 emissions and water consumption can be attained if the existing coal-dominated power generation was substituted by wind power in Inner Mongolia. [ABSTRACT FROM AUTHOR]

The G20, a grouping of the largest developed and developing economies in the world, accounts for almost 86% of global Gross Domestic Product and 76% of global CO 2 emissions. In recent years, the G20 global summit has not only aimed to support an economic agenda, but also to coordinate actions towards a cleaner energy and low-carbon future. However, in different countries driving forces for the change in CO 2 emissions may vary due to being at different stages in their development and being on different development pathways. Due to their increasing share in global greenhouse gas emissions, emerging economies are likely to play a more important role in the future to promote multi-level cooperation on key issues, especially in mitigating climate change. Therefore, a better understanding of driving forces of each country's change in CO 2 emissions is essential to tackle global climate change and to develop a broadly acceptable agenda for climate change mitigation. Using index decomposition analysis, this study aims to identify the main driving forces of CO 2 emissions in the G20 countries. The results show that the driving forces of CO 2 emissions are significantly different in advanced economies versus emerging economies. In general, economic growth was the main factor for increasing CO 2 emissions. Apart from economic growth, population growth shows the strongest effect on CO 2 emission growth in the emerging economies such as Mexico, South Africa, Brazil, Turkey, and Argentina. This growth in emissions was partly compensated by improved energy efficiency and reduced carbon intensity in most advanced economies and some emerging economies, whereas in other G20 countries, such as France, Brazil, Mexico, South Africa, and Saudi Arabia, energy intensity was increasing. Our results imply that different countries require different policies to do justice to the differences in trajectories and factors influencing CO 2 emissions. [ABSTRACT FROM AUTHOR]

Between 2000 and 2010, China׳s electricity production had increased threefold and accounted for 50% of domestic and 12% of global CO 2 emissions in 2010. Substantial changes in the electricity fuel mix are urgently required to meet China׳s carbon intensity target of reducing CO 2 emissions by 40–45% by 2020. Moreover, electricity production is the second largest consumer of water in China, but water requirements vary significantly between different electricity generation technologies. By integrating process-based life-cycle analysis (LCA) and input–output analysis (IOA) and through tracking national supply chains, we have provided a detailed account of total life-cycle carbon emissions (g/kWh) and water consumption (l/kWh) for eight electricity generation technologies – (pulverized) coal, gas, oil, hydro, nuclear, wind, solar photovoltaic, and biomass. We have demonstrated that a shift to low carbon renewable electricity generation technologies, i.e. wind, could potentially save more than 79% of total life-cycle CO 2 emissions and more than 50% water consumption per kWh electricity generation compared to the current fuel mix and technology for electricity generation. If the projected wind farms are built by 2020, Inner Mongolia, one of the water scarce northern provinces, would annually save 179 MT CO 2 (i.e. 44% of Inner Mongolia׳s total CO 2 emissions in 2008) and 418 million m 3 (Mm 3 ) water (18% of its industrial water use in 2008) compared with the same amount of electricity produced from coal. [ABSTRACT FROM AUTHOR]

Since the mid-1990s three Baltic States have significantly increased their per capita gross domestic product (GDP) and at the same time have managed to keep the CO2 equivalent (CO2e) emissions low. We used structural decomposition analysis to identify the drivers of change for CO2e emissions in these countries between 1995 and 2009, a period that includes the collapse of the Soviet Union, restructuring and economic growth and the great recession. The results show that final demand has been the main driving force for increasing emissions in the Baltic States and would have caused an 80%, 64% and 143% emission increase in Estonia, Latvia and Lithuania, respectively, all other factors kept constant. This increase has been partly offset by a declining emission intensity of the economy, especially in Latvia and Lithuania; whereas in Estonia, which has one of the highest emission intensities in Europe, a shift in consumption patterns towards low carbon consumption items and a decarbonizing economic structure were the main balancing factors. It is likely that the Baltic States will experience a continuation of economic growth given their relatively low per capita GDP, which is less than half of the European Union average thus adequate carbon policies are paramount. [Copyright &y& Elsevier]

With rapid economic development, higher income levels, urbanization and other socio-economic drivers, people's lifestyles in China have changed remarkably over the last 50 years. This paper uses the IPAT model (where I =Impact representing CO2 emissions, P =Population, A =Affluence, and T =emission intensity) to analyze how these main drivers contributed to the growth of CO2 emissions over this time period. Affluence or lifestyle change has been variously recognized as one of the key factors contributing to CO2 emissions. Through comparative analysis of the development of five regions in China, we trace lifestyle changes since the foundation of the People's Republic of China (PRC) in 1949 until 2002. We find that household consumption across the five regions follows similar trajectories, driven by changes in income and the increasing availability of goods and services, although significant differences still exist between and within regions due to differential policies in China and different possibilities for social mobility. There are considerable differences between the southeast and northwest and between urban and rural areas. We also found that technological improvements have not been able to fully compensate for the increase of emissions due to population growth and increasing wealth, which is also in line with results from other studies. Finally, this paper emphasizes that developing countries such as China, which is home to 22% of the world population and a growing middle class, and which is on a fast track to modernization, need to ensure that people's lifestyles are changing towards more sustainable ways of living. China has been investing heavily in infrastructure and thus creating the emissions of tomorrow. Thus investing, for example, in public transport and low energy building today will help reduce emissions in the future and will support more sustainable lifestyles. [Copyright &y& Elsevier]

In a globalized economy, production of goods can be disrupted by trade disputes. Yet the resulting impacts on carbon dioxide emissions and ambient particulate matter (PM2.5) related premature mortality are unclear. Here we show that in contrast to a free trade world, with the emission intensity in each sector unchanged, an extremely anti-trade scenario with current tariffs plus an additional 25% tariff on each traded product would reduce the global export volume by 32.5%, gross domestic product by 9.0%, carbon dioxide by 6.3%, and PM2.5-related mortality by 4.1%. The respective impacts would be substantial for the United States, Western Europe and China. A freer trade scenario would increase global carbon dioxide emission and air pollution due to higher levels of production, especially in developing regions with relatively high emission intensities. Global collaborative actions to reduce emission intensities in developing regions could help achieve an economic-environmental win-win state through globalization. Carbon dioxide emissions and air pollution are often assessed on a national or regional level, but little is known about the role of trade structures. Here, a combination of models shows that trade restrictions can lead to massive reduction of gross domestic product in most countries, but also to a reduction of emissions and pollution. [ABSTRACT FROM AUTHOR]

Highlights • The input–output model and ecological network analysis (ENA) are integrated. • The control and dependence conditions of economic sectors in China are analyzed. • The key regions and sectors for carbon mitigation of China are identified. Abstract In this paper, multi-regional input–output analysis (MRIO) and ecological network analysis (ENA) are combined to assess carbon flows within China and identify key regions and sectors in the context of spatial heterogeneity for effective carbon mitigation. An interregional carbon network model is established by articulating the directions and magnitudes of carbon flows based on MRIO. ENA is then used to unveil indirect carbon flows and mutual relationships among regions. The results show that the northwest is the largest controller for most regions in China. Most carbon emissions in the rest of China are induced by the east’s final demand and substantial consumption. In addition, at sectoral level, the control and dependence abilities vary by region in China. This study provides an integrated framework to investigate interregional carbon emission structure, identify efficient pathways for coordinated emission mitigation, and reduce global carbon inequality across regions. [ABSTRACT FROM AUTHOR]

As the Chinese regions become more and more connected to each other and foreign countries, this study aims to address carbon imbalance and outsourcing issues in China. Using a production-based carbon emission inventory and a China-global multi-regional input-output model, this study estimates the consumption-based carbon emissions in 30 Chinese regions in 2007 and 2010. Our results reveal that the carbon imbalances of most Chinese provinces and cities have decreased between 2007 and 2010, but disparities in the regional per capita carbon footprint have widened. Our Structural Path Analysis (SPA) results shows that most Chinese regions have higher ratios of both imported (to consumption-based) and exported (to production-based) carbon emissions in further upstream supplier tiers than that of direct imports and exports in the first tier, thus it’s vital to trace emissions in the upstream supply chain to understand emission outsourcing. Our result from four case study provinces suggests that Beijing should import more electricity products from nearby Hebei and Shandong rather than Inner Mongolia to lower its consumption-based carbon emissions given the smaller emission coefficients of their electricity production. [ABSTRACT FROM AUTHOR]

The carbon tax is a frequently discussed economic instrument for carbon emissions mitigation and prevention of global climate change. However, a range of issues may emerge when introducing a carbon tax; among these issues, the distributional impact has been frequently highlighted as an obstacle to the public acceptance of such a mitigation policy. This literature review focuses specifically on the distributional effects of carbon taxes and contributes to existing studies by providing a classification and discussion on how to comprehensively assess distributional impacts and what measures can be taken to mitigate the potential adverse distributional impact. We confirm that a pure carbon tax without revenue recycling in developed economies tends to be regressive, i.e. lower income households being more affected, while our research does not support the perception that it reveals progressivity in developing countries. In terms of its effects on economic sectors, we find that sectors with higher energy intensity are more affected by a uniform carbon tax, while preferential measures to protect these industries face a trade-off between environmental effectiveness and economic growth. We also stress that different designs for carbon tax mechanisms play a key role in affecting the distributional impacts and impacts in other policy arenas, indicating that trade-offs between efficiency and equity always exist when designing a carbon tax. This study may help to identify the shortcomings of existing designs and puts forward practical implications for future research; moreover, it offers valuable information to help policy-makers to understand the trade-off between equity and efficiency when designing a carbon tax. [ABSTRACT FROM AUTHOR]

Abstract Guangdong is one of China's fastest developing provinces, and thus faces the challenge of reducing CO 2 emissions whilst fostering economic growth. In order to advance the goal of developing a low-carbon economy in China, this study explored influencing factors, change trends, and reduction potential in relation to CO 2 emissions in Guangdong Province using a provincial dataset covering the period 1995 to 2014. We used extended STIRPAT (stochastic impacts by regression on population, affluence, and technology) model and the technique of ridge regression in order to identify key influencing factors behind Guangdong's CO 2 emissions. We also forecasted emission trends and estimated reduction potential in the period 2015–2030. Our empirical results indicate that economic development, population growth, urbanization, fixed asset investment, and industrialization level all positively affected CO 2 emissions during the period studied, while the impacts of energy consumption structure and technology progress were found to be negative. Scenario simulations reveal that the aggregate CO 2 emissions of Guangdong Province will increase continuously up to 2030 under all of the twenty scenarios developed and tested in this study; despite this, we argue that the potential for emission reduction remains large. An optimal scenario was identified after cross-sectional comparison. Our analysis casts new light on the importance of exploring socioeconomic determinants and reduction potential in relation to emissions, in order to plan for rapidly developing regions like Guangdong. The empirical findings have significant implications for the Chinese government in implementing policy measures in order to promote low-carbon development. Highlights • The influencing factors, change trends and reduction potential of CO 2 emissions in Guangdong are explored. • Extended STIRPAT model, ridge regression and scenarios analysis are utilized. • Economic growth, population, urbanization, fix asset investment and industrialization positively affected CO 2 emissions. • Technology and energy consumption structure have a negative impact on CO 2 emissions. • An optimal scenario to promote low-carbon strategy for Guangdong government is identified. [ABSTRACT FROM AUTHOR]