Changes In Carbon Intensity Research Articles

Changes in CO2 Emissions Induced by Agricultural Inputs in China over 1991–2014

Increase in CO2 emissions induced by agricultural inputs in China since its socialist market economic reforms may hinder its sustainable economic development. More attention has been paid to changes in agricultural land use and overall land use benefit; however, relatively less attention has been paid to changes in CO2 emissions induced by agricultural inputs in China since the reforms. The carbon footprint (CF) variation for total agricultural production, the effects of different inputs on the total CF, and the changes in carbon intensity of the agricultural production system were analyzed using national level agrochemical and energy inputs data for the period 1991 to 2014. The total CF had a fluctuating upward trend, which was mainly affected by increases in nitrogen fertilizer input and energy consumption. The carbon intensity in production decreased, which indicated that the agricultural output value per unit of carbon cost gradually increased according to the market demands. However, the increase in agricultural output value per unit of carbon cost cannot outweigh the potential environmental risks induced by the increase in total carbon footprint per unit area of agricultural land. The improvements promoted by the related agricultural policies in China should aim to strike a balance between agricultural economy development and low carbon intensity in area.

Exploring the changes of energy-related carbon intensity in China: an extended Divisia index decomposition

To gain a better understanding of the changes in carbon intensity of China, this study firstly adopted logarithmic mean Divisia index decomposition analysis to decompose the carbon intensity into three driving factors, including emission coefficient effect, energy intensity effect and industrial structure effect. Then, the analysis was furtherly conducted to study the contributions of four economic sectors to the percent change in carbon intensity through each influencing factor by attribution analysis. The results illustrated that the carbon intensity dropped by 46.21 % from 1996 to 2012 mainly caused by the decrease in energy intensity, of which the industrial sector, transportation sector and commercial & service sector were the dominant contributors. The emission coefficient effect and industrial structure effect were equally important in terms of increasing carbon intensity, which principally due to the industrial sector and commercial & service sector. In addition, the energy efficiency of agricultural sector should be furtherly improved, and it is imperative to optimize the energy mix and industrial structure of the industrial sector and commercial & service sector. Therefore, more differentiated policies are urgently required to be implemented in different economic sectors to mitigate the China’s carbon intensity.

Energy consumption and CO2 emissions in Eastern and Central China: A temporal and a cross-regional decomposition analysis

With heavy industrial structure moving westward, to make out the difference of low-carbon development between eastern and central China seems important. For this purpose, this paper compares several representative indicators about energy-related CO2 emissions among nine typical regions from 1990 to 2010. Then two forms of carbon intensity, namely, CO2 emissions per unit of GDP and CO2 emissions per capita are decomposed with a temporal and a cross-regional Logarithmic Mean Divisia Index technique. Thus the roles of driving forces for the changes in carbon intensity are analyzed by region during five study periods, and the main causes for the variation in carbon intensity between eastern and central China in 2010 are also compared. The analysis results show that central China has a challenge to keep the relative lower levels of these two forms of carbon intensity compared with that of eastern China, economic growth in central China needs to choose a low-carbon development road. In terms of eastern China, growth mode of Beijing and its surrounding areas should be integrally planned from the viewpoint of regional coordination. Finally, high-efficient utilization of coal should be promoted in heavy industrial sector for the mitigation of CO2 emissions in central and eastern China.

Changes in carbon intensity in China's industrial sector: Decomposition and attribution analysis

The industrial sector accounts for 70% of the total energy-related CO2 emissions in China. To gain a better understanding of the changes in carbon intensity in China's industrial sector, this study first utilized logarithmic mean Divisia index (LMDI) decomposition analysis to disentangle the carbon intensity into three influencing factors, including the emission coefficient effect, the energy intensity effect, and the structure effect. Then, the analysis was furthered to explore the contributions of individual industrial sub-sectors to each factor by using an extension of the decomposition method proposed in Choi and Ang (2012). The results indicate that from 1996 to 2012, the energy intensity effect was the dominant factor in reducing carbon intensity, of which chemicals, iron and steel, metal and machinery, and cement and ceramics were the most representative sub-sectors. The structure effect did not show a strong impact on carbon intensity. The emission coefficient effect gradually increased the carbon intensity, mainly due to the expansion of electricity consumption, particularly in the metal and machinery and chemicals sub-sectors. The findings suggest that differentiated policies and measures should be considered for various industrial sub-sectors to maximize the energy efficiency potential. Moreover, readjusting the industrial structure and promoting clean and renewable energy is also urgently required to further reduce carbon intensity in China's industrial sector.

Multiplicative decomposition of aggregate carbon intensity change using input–output analysis

Abstract The indicator “aggregate carbon intensity”, such as that given by the ratio of a country’s CO 2 emissions to its GDP, is often used by researchers and policymakers in the study of climate change or environmental performance. A better understanding of the determinants of this indicator is useful in climate policy analysis. This paper introduces four different models to calculate a country’s aggregate carbon intensity using the input–output framework. The models are developed based on different combinations of input–output models, country imports assumptions, and approaches to calculating GDP. Using the four models and multiplicative structural decomposition analysis, the determinants of the aggregate carbon intensity change of China from 2007 to 2010 are studied. The results given by the models are compared and attribution analysis on one of the determinants is conducted. The proposed procedure can be similarly applied to study changes in other aggregate intensity indicators, such as the ratio of energy consumption, water consumption or material use of a country to its GDP.

Diagnostic indicators for integrated assessment models of climate policy

Integrated assessments of how climate policy interacts with energy-economy systems can be performed by a variety of models with different functional structures. In order to provide insights into why results differ between models, this article proposes a diagnostic scheme that can be applied to a wide range of models. Diagnostics can uncover patterns of model behavior and indicate how results differ between model types. Such insights are informative since model behavior can have a significant impact on projections of climate change mitigation costs and other policy-relevant information. The authors propose diagnostic indicators to characterize model responses to carbon price signals and test these in a diagnostic study of 11 global models. Indicators describe the magnitude of emission abatement and the associated costs relative to a harmonized baseline, the relative changes in carbon intensity and energy intensity, and the extent of transformation in the energy system. This study shows a correlation among indicators suggesting that models can be classified into groups based on common patterns of behavior in response to carbon pricing. Such a classification can help to explain variations among policy-relevant model results.

Does Climate Change Mitigation Activity Affect Crude Oil Prices? Evidence from Dynamic Panel Model

This paper empirically investigates how climate change mitigation affects crude oil prices while using carbon intensity as the indicator for climate change mitigation. The relationship between crude oil prices and carbon intensity is estimated using an Arellano and Bond GMM dynamic panel model. This study undertakes a regional-level analysis because of the geographical similarities among the countries in a region. Regions considered for the study are Africa, Asia and Oceania, Central and South America, the EU, the Middle East, and North America. Results show that there is a positive relationship between crude oil prices and carbon intensity, and a 1% change in carbon intensity is expected to cause about 1.6% change in crude oil prices in the short run and 8.4% change in crude oil prices in the long run while the speed of adjustment is 19%.

Analysis of Key Drivers on China's Carbon Emissions and Policy Rethinking Based on LMDI: 1995–2010

Economic policy and energy policy are two major factors of energy consumption and carbon emissions. The economic factor is external and energy supply structure and efficiency are intrinsic factors. Based on a carbon emissions completely decomposed analysis model, the logarithmic mean Divisia Index (LMDI) system analyzes the impact of carbon emission changes and the contribution rate in China from 1995 to 2010. The decomposition factors include four parts: economies of scale, structure effect, energy intensity effect and carbon intensity effects. Model results show that the contribution rate of the four effects is different and from 1995 to 2010 the greatest factors impacting increases in carbon emissions were economic development (contribution rate of 155%) and industrial structure change (contribution rate of 10.6%). The reduction in carbon emissions was mainly the result of a decline in energy intensity (contribution rate of -63.7%). The increase in carbon emissions in recent years is the result of changes in major economies of scale with 168.2% contribution rate, changes in carbon intensity (contribution rate of 4%) and industrial restructuring (contribution rate of 1.3%) have also contributed to increasing carbon emissions. Energy intensity declined only played a role in reducing carbon emissions (contribution rate -73.5%). These results suggest that China needs to rethink industrial policy and energy development measures, strengthen future energy saving and emission mitigation policies and strengthen investment in low—carbon energy technologies and policy support.

The Impact of Development on CO₂ Emissions: A Case Study for Bangladesh Until 2050

Bangladesh, a country with a population of 160 million, is currently contributing 0.14 percent to the world’s emission of carbon dioxide (CO₂). However, mostly due to a growing population and economic growth (which both lead to an increase in energy consumption), Bangladesh’s share in CO₂ emissions is – despite the increasing use of alternative energy – expected to rise sharply. This study uses the example of Bangladesh to illustrate the impact of low-income countries’ energy neutral development on global CO₂ emissions in 2050 by using a set of alternative assumptions for population growth and GDP growth. It also shows how complex the determinants for (a) gains in energy efficiency and (b) changes in carbon intensity are in low-income countries.

Strategic environmental disclosure: Evidence from the DOE's voluntary greenhouse gas registry

Although mandatory disclosure programs have been studied extensively, strategic voluntary environmental disclosures by firms are not well understood. We study the motivations for and impacts of firms' strategic disclosure of greenhouse gas reductions to the US government. We first model firms' joint abatement and disclosure decisions, incorporating both economic and political incentives. We then use data from the Department of Energy's Voluntary Greenhouse Gas Registry to compare reported reductions to actual emissions. We find that participants in the program engage in highly selective reporting: in the aggregate, they increase emissions over time but report reductions. In contrast, non-participants decrease emissions over time. Participants tend to be large firms facing strong regulatory pressure; pressure from environmental groups reduces the likelihood of participation, suggesting such groups viewed the program as a form of greenwash. Participating in the 1605(b) program had no significant effect on a firm's changes in carbon intensity over time.

Changes in carbon intensity in China: Empirical findings from 1980–2003

China experienced sustainable, rapid economic growth over the period 1980–2003 but, at the same time, energy-related carbon intensity showed a downward trend. It begs the question, therefore, what factors were driving this decline in carbon intensity and will this decline be maintained in future? Moreover, what measures can be adopted to ensure a continual decline in carbon intensity? These questions led to increased research in the factors governing CO 2 emission in China. This paper quantifies the driving force behind China's primary energy-related carbon intensity and measures the material production sectors' final energy-related carbon intensity. Our results show that the overwhelming contributor to the decline of energy-related carbon intensity was the reduction in real energy intensity. However, policies that focus only on the decline in energy intensity are insufficient to further decrease carbon intensity. The change of primary energy mix can improve the decline of carbon intensity. This should focus on the material production sectors' development strategies and final energy use. Greater emphasis should be given to secondary industry, which needs national and regional governments' policy support.