Carbon Intensity Target Research Articles

A general equilibrium assessment of economic impacts of provincial unbalanced carbon intensity targets in China

It is necessary to measure the economic impacts of the attempts to achieve China’s carbon emission intensity reduction target. Consequently, this study analyzes the economic impacts of the differentiated CO2 intensity targets between Guangxi Province and the rest of China. An improved two-region computable general equilibrium model with eight scenarios is used. Our results show that different CO2 intensity targets in different regions will affect Guangxi’s GDP, carbon price, welfare, and output. The highest reduction target of 75% in the P75C65 scenario in Guangxi will lead to a cost of 0.42% in per capita GDP loss, 0.51% of welfare loss, and a carbon price of 49.4 USD/t. In addition, the output of the energy-intensive sectors is most vulnerable to carbon mitigation policy. The two mechanisms that affect economic indicators are price and scale effects. Under the P75C65 scenario, sectors such as vehicle manufacturing are winners and are affected by both price and scale effects in Guangxi, with export, provincial outflow, domestic supply, and output decreasing by 1.64%, 0.88%, 0.93%, and 0.93%, respectively. In contrast, sectors such as agriculture are losers and are affected by the scale effect, with these four indicators increasing by 1.25%, 0.97%, 0.59%, and 0.73%, respectively. These findings provide valuable insights for policy makers who wish to allocate provincial reduction targets and achieve co-benefits between the economy and the environment.

Does industrial green transformation successfully facilitate a decrease in carbon intensity in China? An environmental regulation perspective

Global climate change caused by carbon emissions poses a severe challenge to human economic and social development. The Chinese government has committed to a series of emission reduction initiatives to achieve carbon intensity targets by actively promoting the green transformation of the industrial sector—the main source of energy consumption and environmental pollution. This transformation has been ongoing for more than five years, and the effects, problems and experiences are worth discussing. Therefore, using province-level panel data for China's industry from 2010 to 2015, we systematically analyze the regional structure and developmental trend of industrial green transformation and empirically investigate its dynamic threshold effects on carbon intensity under different degrees of environmental regulation. The results show that China's industry has gradually undergone a green transformation, which has significantly reduced pollution emissions. However, the process has a large developmental scope due to regional heterogeneity and fluctuation characteristics. Interestingly, the impact of the industrial green transformation on carbon intensity is limited by the "critical mass" of environmental regulations. Paradoxically, weak environmental regulation significantly facilitates a decrease in carbon intensity through industrial green transformation. Once environmental regulation surpasses a critical level, the role of this transformation in CO2 reduction is weakened, resulting in a failure to decrease carbon intensity. We provide insights into the driving factors that reduce carbon intensity and improve our understanding of the driving forces, paths and policy designs needed to successfully reach carbon intensity targets.

The role of energy efficiency improvement and energy substitution in achieving China’s carbon intensity target

Energy efficiency improvement and energy substitution are not only main ways to reduce energy consumption and carbon dioxide emissions in China, but also play important roles in achieving China’s international commitments and coping with the challenges of global climate change. From the beginning of the 11th Five-Year Plan (FYP), China has adopted a series of policies and measures in ways of improving energy efficiency, optimizing energy structure, developing low-carbon city pilots and so forth. This study takes use of the China-in-Global Energy Model to analyze the future carbon dioxide emissions and energy consumption under different scenarios, and then calculates the contributions of energy efficiency improvement and energy substitution to realizing the mitigation target of China’s National Determined Contributions. The results show that energy efficiency improvement plays more important role in reducing carbon intensity than energy substitution. But as time goes on, the contribution of energy substitution will increase while the effect of energy efficiency improvement will diminish gradually. In addition, energy substitution can play a greater role in achieving China’s climate pledge when increasing the strength of carbon emissions reduction.

Opportunities for Energy Storage: Assessing Whole-System Economic Benefits of Energy Storage in Future Electricity Systems

Any Cost-effective transition toward low-carbon electricity supply will necessitate improved system flexibility to address the challenges of increased balancing requirements and degradation in asset use. Energy storage (ES) represents a flexible option that can bring significant, fundamental economic benefits to various areas in the electric power sector, including reduced investment requirements for generation, transmission, and distribution infrastructure as well as reduced system operation and balancing costs. The additional flexibility offered by ES could also significantly reduce the requirement for investment in low-carbon generation capacity while achieving the established carbon intensity targets. Moreover, ES may present significant option value, as it can provide flexibility for dealing with uncertainty in future system development.

Path analysis of factors impacting China's CO2 emission intensity: Viewpoint on energy

To explore the means of achieving carbon intensity targets in China, this study empirically discusses the key factors influencing the CO2 emission intensity and the causal relationships among these factors based on a path analysis using panel data. Six factors are included in the analysis: gross domestic product (GDP) per capita (A), technology effect (TE), energy price (EP), industrial structure (IS), energy structure (ES) and foreign direct investment (FDI). The study period is divided into three stages: 1991–2002, 2003–2014 and 1991–2014. Improving T is the most significant way to decrease the emission intensity, followed by FDI. In each stage, the effects of A, EP, ES and FDI on the emission intensity mainly come from indirect contributions through TE and IS, and the effects of TE and IS arise primarily through direct contributions. Improving A promotes the development of TE in each stage and the adjustment of IS in 1991–2002 but increases the proportion of secondary industry in 2003–2014. A rising EP negatively affects TE in 2003–2014. Improving the FDI promotes the development of TE in each stage but is not conducive to the adjustment of IS in 2003–2014.

Carbon intensity changes in the Asian Dragons. Lessons for climate policy design

There is a growing political interest in carbon intensity targets because they are the basis for climate pledges from relevant developing countries such as China. They may be also the basis for policy designs in developed countries like EU members. This paper develops a comprehensive econometric study on the main drivers of national emissions intensity in emerging countries in East Asia. This regional focus responds to their pivotal position in global economic growth and remarkable trends in carbon emissions intensity. The main hypothesis of this paper is that the nature of economic growth has a major effect on carbon intensity trends that deserves some attention. Accordingly, the novelty of this paper is to examine the contribution of “intensive” and “extensive” GDP growth for carbon intensity abatements. Labour productivity is revealed to be the main factor responsible for major carbon intensity reductions by Asian Dragons. Whereas household energy per capita and industrial energy per worker contributed in the opposite direction. Consequently, intensity targets may become “meaningless” for real climate action contributions if they do not take into account labour productivity trends.

Estimating health co-benefits of greenhouse gas reduction strategies with a simplified energy balance based model: The Suzhou City case

To translate scientific evidence into policy decisions, a universal, integrate and rapid–calculation model is needed for estimating the air quality associated health co–benefits of climate change mitigation strategies. This study developed such a greenhouse gas policy assessment model (GHG–PAM) based upon the annually published energy balances tables. To examine the applicability of this model, we select Suzhou, one of the national low-carbon pilot cities located in the east of China, as a case study. It was determined that carbon dioxide (CO2) emissions in Suzhou would increase from 148 million tons in 2010 to 297, 284, 180, and 173 million tons in 2020 under Business As Usual (BAU), Industrial Structure Dominated (ISD), Technology Dominated (TD), and Integrate Carbon Reduction (ICR) scenarios, of which only ICR scenario meets its carbon intensity target. Under the ICR scenario, the implementation of greenhouse gas policies reduces the air pollution associated burden of diseases, measured by Disability-Adjusted Life Years (DALY), by 44.1% in 2020 compared to BAU, although even with this scenario the burden of diseases would still be higher than that in 2010. In addition, the model can also identify sectors and industries with greater health co–benefits per unit CO2 emission reduction, which provides a useful guide for Suzhou's climate policy makers to formulate cost–effective measures. However, it should be carefully used where there is a mismatch between sectors with high potential for CO2 emission reductions and sectors with high health co–benefits per unit CO2 emission reduction.

Potential assessment of optimizing energy structure in the city of carbon intensity target

Optimizing energy structure to reduce carbon intensity is an effective way to build the low-carbon city development model. To assess the contribution potential of energy structure optimization in order to achieve the carbon intensity target in Hangzhou City, the component data prediction, scenario prediction, GMI (1, 1) model and other multidisciplinary approaches were adopted to predict the carbon intensity trends in the above said City from 2014 to 2020. This contribution potential is evaluated based on 9 kinds of combined scenarios. The results show that: 1. Under the same economic growth rate, with the increase in energy structure adjustment range, the carbon intensity “decline range” becomes larger, and the higher is the “contribution potential” of energy structure optimization to achieve the carbon intensity target. 2. Within the same range of energy structure optimization, the economic growth rate is lower, the carbon intensity “decline range” is smaller, and the “contribution potential” of energy structure optimization is higher for the same carbon intensity objective. By optimizing energy structure and industrial structures adjustment, the technology upgrading for carbon emissions and the scientific and technological level of the systematic industrial policies are more conducive to supporting the realization of low-carbon city.

Can China achieve its 2020 carbon intensity target? A scenario analysis based on system dynamics approach

Since Chinese government put a target to decrease carbon intensity 40–45% in 2020 than that of 2005, a series of emission mitigation measures has been implemented. Against this backdrop, we established a system dynamics model to investigate the energy consumption, CO2 emission and mitigation options in China. The results show that the carbon intensity will reduce by 22.68%, 26.84%, 43.77%, and 46.65% in BaU (Business as usual), NEP (New energy policy), CTP (Carbon tax policy) and IP (Integrated policy) scenarios in 2020 compared with 2005. Obviously, Chinese government can accomplish the target under CTP and IP scenarios. Moreover, the “inflection point” in CTP and IP scenarios reveals the decision-making process between tax burden and emission reduction behavior. A brief analysis of interactive effect is accomplished by equilibrium theory and simulation results. It shows that the interactive effect of two policies, which act on the same object with same action direction, is weaker than the aggregation of two separated effects, whereas it is larger than any individual effect. In a nutshell, these findings are helpful for policymakers to optimize their policy decision-making to cut CO2 emissions.

Carbon emission targets and decoupling indicators

Carbon intensity targets are part of the emission reduction metrics used in some of Copenhagen pledges and of Intended Nationally Determined Contributions. One of the alleged features of such target format is to secure a reduction in emissions’ intensity in order to decouple greenhouse gases generation from economic activity. This article compares the decoupling indicators most commonly used in the literature and shows that, there are more cases to analyze decoupling than those usually considered and that sometimes there is agreement but there can also be disagreement among indicators. Decoupling is not a goal in itself, diminishing emissions is. In that sense, it becomes clear here that strong delinking of emissions from GDP is better than weak decoupling in growing economies (because emissions’ intensity decreases in both cases but emissions only diminish in the former), but this ranking does not hold in recessive economies. When the economy is in recession, weak negative decoupling over scores strong negative decoupling since, only in the former, emissions decrease. Nevertheless, the best possible state in an economy in retraction is recessive decoupling, that is “green degrowth” (emissions, GDP and emissions’ intensity all decrease). In the existing literature, decoupling indicators have been employed to analyze countries, regions, cities, or sectors with stable growth and some of the decoupling degrees were detected. Here, Argentina is used for illustration purposes since it has the advantage to be the first nation to design a GDP related carbon emissions’ target and be at the same time a very unstable country. The latter characteristic allows finding almost all cases of decoupling when considering emissions from 1990 to 2012.

Estimating the Contribution of Industry Structure Adjustment to the Carbon Intensity Target: A Case of Guangdong

Industry structure adjustment is an effective measure to achieve the carbon intensity target of Guangdong Province. Accurately evaluating the contribution of industry structure adjustment to the carbon intensity target is helpful for the government to implement more flexible and effective policies and measures for CO2 emissions reduction. In this paper, we attempt to evaluate the contribution of industry structure adjustment to the carbon intensity target. Firstly, we predict the gross domestic product (GDP) with scenario forecasting, industry structure with the Markov chain model, CO2 emissions with a novel correlation mode based on least squares support vector machine, and then we assess the contribution of industry structure adjustment to the carbon intensity target of Guangdong during the period of 2011–2015 under nine scenarios. The obtained results show, in the ideal scenario, that the economy will grow at a high speed and the industry structure will be significantly adjusted, and thus the carbon intensity in 2015 will decrease by 25.53% compared to that in 2010, which will make a 130.94% contribution to the carbon intensity target. Meanwhile, in the conservative scenario, the economy will grow at a low speed and the industry structure will be slightly adjusted, and thus the carbon intensity in 2015 will decrease by 23.89% compared to that in 2010, which will make a 122.50% contribution to the carbon intensity target.

Efficiency or Equity? Simulating the Carbon Emission Permits Trading Schemes in China Based on an Inter-Regional CGE Model

Energy conservation and greenhouse gas (GHG) abatement have been included in the national development strategy of China. However, the rigidity in command-and-control mechanisms and arbitrariness in assignment of GHG abatement burden across regions have caused unnecessary losses in both economic efficiency and social equity. In this paper, we use an Inter-Regional Dynamic CGE (IRD-CGE) model to simulate economic and welfare impacts of climate policies on national and regional level, including carbon intensity targets, regional emission constraints and cap-and-trade mechanism. Comparison among alternative emission reduction policy mechanisms indicates that emission trading scheme can not only moderate the economic and social welfare losses, but also improve social equity by decoupling the allocation of emission permits from economic optimization of emission reduction scheme. From this perspective, emissions trading bridges the concerns for economic efficiency and social equity, since emission permits could be reallocated as an income transfer so as to promote inter-regional equity, while economic efficiency is maintained.

Exploring linkages among China's 2030 climate targets

China published its Intended Nationally Determined Contribution (INDC) to the UNFCCC on 30 June 2015. In this document, China promised to reach its CO2 emissions peak no later than 2030, reducing its carbon intensity by 60–65% by 2030, relative to the 2005 level, and to increase the share of non-fossil fuels in primary energy consumption to 20% by 2030. Using a simple method and official data, this article aims to explore the linkages among these three targets. First, it shows that as long as China achieves its 2030 non-fossil fuel target, its carbon emissions peak can be attained prior to 2030. Second, it provides a panoramic view of the link between carbon intensity and non-fossil fuel targets with different levels of GDP growth rate and energy elasticity. This article also presents further conclusions based on this finding: first, that a GDP carbon intensity target may help to control the absolute level of the carbon emissions peak, but it could be inconsistent with the development of non-fossil fuel power; and second, that a GDP energy intensity objective, together with a non-fossil fuel target, is necessary to ensure target coherency.Policy relevanceThe carbon emissions peak and the non-fossil fuel share of the energy mix can be considered as two key pillars of China's post-2020 climate pledges under the UNFCCC negotiation framework. It is therefore important to understand the relation between the two targets. This article uses a very simple non-modelling approach to demonstrate the implications of the achievement of China's non-fossil fuels target in terms of its carbon emissions peak and the linkage between achieving carbon intensity and non-fossil fuel targets under different growth and energy elasticity assumptions. Without focusing on the relevance of peak level or time, the authors illustrate the relationship between the 2030 non-fossil fuels target and the carbon emissions peak, highlighting the potential inconsistency between GDP carbon intensity and non-fossil fuels targets. These findings should have both political and academic uses to enable further clarification and analysis of China's INDC.

Impacts of low-carbon power policy on carbon mitigation in Guangdong Province, China

This paper analyzes the impacts of the low-carbon policy in the power sector of Guangdong Province in China on its energy and carbon emission targets by 2020, as well as their costs and co-benefits, using a regional CGE model with seven scenarios: business as usual (BaU), renewable energy (RE), renewable energy and natural gas (RE–NG), CAP only (CAP), CAP and RE–NG (CAP–RE–NG), carbon emission trading (ETS), and ETS with RE–NG (ETS–RE–NG). Analysis results reveal that provincial energy and carbon intensity targets can be achieved in the assumed carbon mitigation scenarios with carbon cap, ETS, and clean energy development policies. While the carbon constraint exerts negative impacts on the economy, GDP loss could be lowered by the ETS and RE policies. The RE scenario is more economically efficient than the ETS scenario, and coupling the RE and ETS scenarios appears to be the most economically efficient scenario to achieve the desired carbon and energy intensity targets. One of the benefits of the low-carbon policy is its improvement of the energy security of Guangdong in terms of reduced reliance on external coal and oil; in particular, Guangdong coal consumption could peak in 2017–2019.

Can China achieve its carbon intensity target by 2020 while sustaining economic growth?

In 2009, the Chinese government announced its carbon intensity target for 2020. Can China achieve this carbon intensity while sustaining economic growth? To address this essential issue, the contributions of this study lie in three aspects. First, we quantitatively capture the relationship between China's economic growth and energy consumption using the cointegration theory, and predict China's energy consumption by 2020 according to different economic growth targets. Second, we forecast China's energy structure in 2020 using compositional data and ARIMA models under different scenarios. Third, we deduce China's CO2 emission and carbon intensity in 2020. Furthermore, we investigate whether (or not) China can realize the carbon intensity target in premise of ensuring economic growth, and evaluate the contribution of the energy structure's adjustment to meeting this target. Finally, we put forward some relevant policy implications for achieving China's carbon intensity target.

China’s regional CO2 emissions reduction potential: A study of Chongqing city

Since China has put forward a series of obligatory Greenhouse Gas (GHG) emission reduction targets, provincial governments have issued provincial-level “Twelfth Five-Year” carbon intensity (CO2 emissions/GDP) reduction targets. There are 653 cities distributed throughout the eastern, middle and western parts of China, and each region has different quotas and paths to reduce GHG emissions, while the western part has greater challenges than the others. This paper predicts CO2 emission reduction potential of Chongqing, assesses the difficulty of achieving its CO2 emission reduction targets, and analyses its low-carbon transition path. The results show that the carbon intensity of Chongqing in 2020 will range from 1.58 to 1.75 ton CO2/104 RMB, and there exist some scenarios with strong potential to meet carbon intensity reduction targets for 2020. Improved technology, energy efficiency, the optimization of energy input mix, and the adjustment of industrial structure are suggested to be major strategies to reach Chongqing’s carbon intensity targets.

Impacts of carbon trading scheme on air pollutant emissions in Guangdong Province of China

This study aims to assess the impacts of carbon emission trading scheme (ETS) policy on air pollutant emission reduction in Guangdong (GD) Province, especially with respect to the embedded air pollutant emission flow caused by carbon ETS. A Computable General Equilibrium (CGE) model is constructed to project the local emission trajectory of CO2 and air pollutants under business as-usual (BaU) and policy scenarios in GD province and the rest of China from 2007 to 2020. To achieve the energy and carbon intensity targets, the carbon constraint and ETS policy are employed to promote energy saving and CO2 emission reduction. The simulation results show that the carbon ETS has the co-benefits of reducing SO2 and NOx emissions by 12.4% and 11.7% in 2020 compared with the BaU scenario. Along with the carbon trading volume of 633milliontons created by the ETS scenario, an embedded amount of 38,000tons of air pollutants is exchanged among carbon trading sectors, which valued about 50 million USD. Although the current carbon and air pollutant emission markets are independent from each other, the evaluation of the co-benefits needs to be considered further in the policy making process.

Carbon Emissions of Chiller Systems in Hong Kong Hotels under Climate Change

ABSTRACTBuilding energy simulation is a common technique to forecast future energy use and develop strategies for meeting carbon reduction targets. The purpose of this study is to analyze the trend of electricity use and carbon emissions of chiller systems—the most energy intensive type of system—using building energy simulation for hotels in subtropical zones under climate change. Based on a typical meteorological year weather file for subtropical Hong Kong, weather data were forecasted for climate change scenarios in 2020, 2050 and 2080. The building simulation program EnergyPlus was used to model a reference hotel with two typical chiller system designs. Simulation results show that a system capacity extension by up to 5% could be considered to satisfy the increasing cooling demand for a 15-year operating span. Various strategies have been discussed for chiller systems to reduce carbon emissions by the demand side. A rigorous carbon intensity target by power companies should be in place to reduce the i...

The energy and water nexus in Chinese electricity production: A hybrid life cycle analysis

Abstract 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.

An index decomposition analysis of China's interregional embodied carbon flows

Abstract Being the largest emitter of carbon dioxide in the world, China also possesses enormous regional disparities in terms of carbon dioxide emissions. Significant carbon emission flows from developed regions to undeveloped regions through inter-regional trade have been observed in China. In order to examine the determining factors of such virtual carbon flows, an index decomposition analysis was conducted to investigate emissions embodied in trades among eight regions, which were grouped with recourse to the latest available provincial I/O tables in China. This paper finds that trade balance and energy intensity are two most salient factors with regard to interregional carbon flows. In the largest outsourcing region, the northwest region of China, the two factors account for 35.6–59.1% of the total carbon surplus, respectively. Thus, China is experiencing more ‘efficiency losses’ than ‘efficiency gains’ resulting from carbon transfers. In order to switch to a future “efficiency gain” situation, policies and supports for technology innovation in and transfer to inland regions, as well as enhanced technology standards for expanded carbon intensive productions in less developed regions are suggested to complement the current policy of carbon intensity target.