Energy Intensities and Carbon Dioxide Emissions in a Social Accounting Matrix Model of the Andalusian Economy

Population growth and trends are centrally important to the environment because it helps to determine the environmental impact of human activities. In this study, the World Bank database has been used. Here, carbon dioxide (CO2) emissions, and energy intensity (EI) are considered as environmental indicators. The population indicators are the proportion of the population aged 15-64 years, and the percentage of the urban population. The Gross Domestic Product (GDP) is considered a development indicator in a country. This study tries to identify the association between population environment and development. Correlation analysis has been employed to know association and Path analysis is used to determine the important factors for environmental impacts such as carbon dioxide (CO2) emissions. The result presents that the zero-order correlation exists among energy intensity (EI), the proportion of the population aged 15-64 (P15-64), urbanization (UR), gross domestic product (GDP) per capita (US$), total population (P) ) and carbon dioxide (CO2) emission in Bangladesh and India. It is observed that 8 paths for Bangladesh and 7 paths for India out of each 12 hypothesized paths are found to be statistically significant. In Bangladesh, the total effects of exogenous variables like as energy intensity (X1) and population aged 15-64 (X2) are observed negative direction on carbon dioxide emissions (X6) and the remaining variable like as urbanization (X3) is observed as positive direction on carbon dioxide emissions. However, in India total effects of these two exogenous variables population aged 15-64 (X2) and urbanization (X3) are observed positive direction on carbon dioxide emissions (X6) and the remaining variable like as energy intensity (X1) is observed negative direction on carbon dioxide emissions (X6). The total effects of endogenous variables like as GDP per capita (X4) show a negative direction on carbon dioxide emissions and population (X5) shows a positive direction on carbon dioxide emissions. The study demonstrates that CO2 emission is important for environmental impact in Bangladesh and India. There is a strong association between population, GDP per capita, energy consumption and urbanization and CO2 emission in Bangladesh and India. The factors of CO2 emissions play an important role in environmental degradation. Thus, attention should be focused on using low energy consumption, and proper urbanization, particularly on modern technology which assures fewer uses of CO2 emissions in Bangladesh and India.

Low-carbon production of iron and steel: Technology options, economic assessment, and policy

This chapter focuses on the social accounting matrix and economic models constructed from social accounting matrices. It reviews the flow of income as it is captured in a social accounting matrix (SAM) representative of the United States economy. An example of the improvement in multiplier interpretation that is offered with SAM models comes from the coefficients in the household columns. In order to construct an economic model which shows the relationship between demand shocks and changes in domestic output it is necessary to first purge the commodity rows of their import content. The basic structure of a SAM follows from the National Income and Product Accounts. The institution accounts receive factor income from the value added accounts and distribute it to government, household, or capital accounts. The chapter concludes with a discussion and comparison between SAM coefficients and Input/Output (IO) Leontief coefficients when households are treated as endogenous in IO models.

A dynamic STIRPAT model used in the current study is based on panel data from the eight most populous countries from 1975 to 2020, revealing the nonlinear effects of urbanization routes (percentage of total urbanization, percentage of small cities and percentage of large cities) on carbon dioxide (CO2) emissions. Using “Dynamic Display Unrelated Regression (DSUR)” and “Fully Modified Ordinary Least Squares (FMOLS)” regressions, the outcomes reflect that percentage of total urbanization and percentage of small cities have an incremental influence on carbon dioxide emissions. However, square percentage of small cities and square percentage of total urbanization have significant adverse effects on carbon dioxide (CO2) emissions. The positive relationship between the percentage of small cities, percentage of total urbanization and CO2 emissions and the negative relationship between the square percentage of small cities, square percentage of total urbanization and CO2 emissions legitimize the inverted U-shaped EKC hypothesis. The impact of the percentage of large cities on carbon dioxide emissions is significantly negative, while the impact of the square percentage of large cities on carbon dioxide emissions is significantly positive, validating a U-shaped EKC hypothesis. The incremental effect of percentage of small cities and percentage of total urbanization on long-term environmental degradation can provide support for ecological modernization theory. Energy intensity, Gross Domestic Product (GDP), industrial growth and transport infrastructure stimulate long-term CO2 emissions. Country-level findings from the AMG estimator support a U-shaped link between the percentage of small cities and CO2 emissions for each country in the entire panel except the United States. In addition, the Dumitrescu and Hulin causality tests yield a two-way causality between emission of carbon dioxide and squared percentage of total urbanization, between the percentage of the large cities and emission of carbon dioxide, and between energy intensity and emission of carbon dioxide. This study proposes renewable energy options and green city-friendly technologies to improve the environmental quality of urban areas.

Some previous studies calculated backward linkage and forward linkage effects of exogenous change in output capacity using mixed endogenous–exogenous models within an input–output (IO) or social accounting matrix (SAM) framework. For calculating forward linkage effects, these studies used the supply-drive(Ghosh in Economica 25(1):58–64, 1958) approach. However, the Ghosh approach has been criticized based on its problematic theoretical interpretation. This study uses an Alaska SAM model to estimate the regional economic impacts of restricting catch of Pacific cod and Atka mackerel in the Aleutian Islands in order to protect Steller sea lions. This study overcomes the problem of calculating forward linkage effects in the previous studies by running the SAM model with (1) changes in output converted to final demand shocks and (2) regional purchase coefficients for all the directly impacted industries (fish harvesting and processing industries) set equal to zero. The impacts of the shift in harvest opportunities in response to the Steller sea lion protection measures are displayed in terms of changes in output, employment, value added, household income, and state and local government revenue.

A social accounting matrix (SAM) model for Alaska is constructed to investigate the role of the state’s seafood processing industry. The SAM model enables incorporation of the unique features of Alaska economy such as (a) the existence of a large nontraditional economic base, (b) a large leakage of labor income, and (c) a very large share of intermediate inputs imported from outside the state. The role of an industry in an economy with these features cannot be examined correctly within an input–output framework, which is the method most often used for examining the importance of an industry to a region. Taking an export base view of the economy, we found seafood processing to be an important industry, generating 4.5% of the state’s total employment. While an important driver of the state’s economy, the industry has the smallest SAM multiplier mainly due to a large leakage of labor earnings and a large share of imported intermediate inputs. We also found that nontraditional economic base components such as (a) federal transfers to state and local governments, and (b) federal transfers, permanent fund dividend (PFD) payments, and other extra-regional income received by households generate about 26% of the state’s total employment and earnings.

This study applies a descriptive approach, the approach in question aims to provide an explanation or description of the application of the I-O method in the defense sector through the modification of the Social Accounting Matrix (SAM) Model, while the type of research applied in this study is descriptive qualitative research, namely interpreting or describing about everything that exists and is related, this interpretation relates to existing relationships or conditions, the process of an ongoing situation, effects or consequences that occur, opinions that are developing, or trends that are growing. In the Input–Output Method, the basic table is a transaction table consisting of: input coefficient table (coefficient matrix), multiplier table, attractiveness index table and driving force index as well as supporting tables and other analysis tables depending on the breadth of the field to be discussed. This study concludes that the I-O model is applied to the defense sector by developing the SAM (Social Accounting Matrix) model. This model is an extension of the I-O model which includes economic sub-sectors or more detailed sub-sectors.

This book studies the impact of public expenditure allocations in achieving income equality goals in Malaysia. The book examines the initial functional and institutional distribution of income across different institutional agents and sectors and evaluates the impact of the public expenditure policies in reducing the inter-ethnic and rural–urban disparity. Since Malaysia has made enormous progress in eliminating poverty, the authors suggest that a change of emphasis in the public expenditure policy may now be called for. They present evidence on the importance of public expenditure in improving income inequality and examine the initial functional and institutional distribution of income across different institutional agents and sectors. The development of the Social Accounting Matrix (SAM) model that presents both economic and social statistics in an economy can be served as a useful tool of this work. The SAM model is used to evaluate the impact of the public expenditure policies in reducing inter-ethnic and rural–urban disparity. A comprehensive source of information on how to deal with inequality economic challenges, the book will be of interest to economists and researchers on Southeast Asian Studies.

This paper analyzes the spatial and temporal distribution of CO2 emissions intensity and energy intensity in China by using spatial measuring method from 2000 to 2013 and estimates the potential for CO2 emissions reduction. The results obtained in this study include: (1) Both CO2 emissions intensity and energy intensity are declining; (2) the spatial distribution of carbon emission intensity and energy intensity in China shows the characteristics of lower from north to south; (3) China’s total growth of energy consumption and carbon emissions is clearly slowing, which will peak before 2030; the carbon emission reduction potential in China is great with 167,316.91 million tons, and Shanxi, Inner Mongolia and Hebei have the greatest potential to reduce CO2 emissions with 29,885.8 Mt, 32,704.49 Mt and 34,222.1 Mt, respectively; (4) the differences of CO2 emissions intensity and energy intensity among provinces are distinctive. This study can provide a reference for the sustainable development of China’s energy and environment.

Carbon emission reduction has become a worldwide concern on account of global sustainability issues. Many existing studies have focused on the various socioeconomic influencing factors of carbon dioxide (CO2) emissions and the corresponding transmission mechanisms, while very few models have unified the scale effect, structure effect, and technique effect in the context of China. This paper attempted to analyze the impact of economic growth, industrial transition, and energy intensity on CO2 emissions in China by constructing an autoregressive distributed lag (ARDL) model. The results showed that there are long-term cointegration relationships between the three factors mentioned above and CO2 emissions. There is an inverted U-shaped relationship between economic growth and CO2 emissions, which not only verifies the environmental Kuznets curve (EKC) hypothesis, but also upholds the scale effect. In addition, the proportion of added value of secondary industry and energy intensity has significant positive impacts on CO2 emissions. On one hand, this confirms the structure effect and technique effect; on the other hand, it implies that the reduction effect is the dominant effect in the case of China, instead of the rebound effect. This paper is expected to make a valuable contribution to research in the field of sustainable development by providing both theoretical support and implementation of path choice for CO2 reduction in China.

Since the reform and opening-up, China’s CO2 emissions have increased dramatically, and it has become the world’s largest CO2 emission and primary energy consumption country. The manufacturing industry is one of the biggest contributors to CO2 emission, and determining the drivers of CO2 emissions are essential for effective environmental policy. China is also a vast transition economy with great regional differences. Therefore, based on the data of China’s provincial panel from 2000 to 2013 and the improved STIRPAT model, this paper studies the impact of economic growth, foreign direct investment (FDI) and energy intensity on China’s manufacturing carbon emissions through the fixed-effect panel quantile regression model. The results show that the effects of economic growth, FDI and energy intensity on carbon emissions of the manufacturing industry are different in different levels and regions, and they have apparent heterogeneity. In particular, economic growth plays a decisive role in the CO2 emissions of the manufacturing industry. Economic growth has a positive impact on the carbon emissions of the manufacturing industry; specifically, a higher impact on high carbon emission provinces. Besides, FDI has a significant positive effect on the upper emission provinces of the manufacturing industry, which proves that there is a pollution paradise hypothesis in China’s manufacturing industry, but no halo effect hypothesis. The reduction of energy intensity does not have a positive effect on the reduction of carbon emissions. The higher impact of the energy intensity of upper emission provinces on carbon emissions from their manufacturing industry, shows that there is an energy rebound effect in China’s manufacturing industry. Finally, our study confirms that China’s manufacturing industry has considerable space for emission reduction. The results also provide policy recommendations for policymakers.

This paper analyzes the changes in carbon dioxide (CO2) emissions related to energy consumption in the Polish agricultural sector between 2000 and 2019. Based on the Logarithmic Mean Divisia Index (LMDI), the changes in agricultural CO2 emissions are viewed in the context of changes in six factors, i.e., CO2 emission intensity, substitution of fossil fuels, penetration of renewable energies, energy intensity, labor productivity and number of employees. The analysis demonstrated that total energy consumption declined over the study period; this was related to a reduction in the intake of energy derived from solid fossil fuels (−1.05%), crude oil (−1.01%), electricity (−4.89%), and heat (−1.37%), and to an increased consumption of natural gas (5.78%) and biofuels (0.82%). Furthermore, it follows from the analysis that changes in CO2 emissions witnessed in that period were consistent with changes in energy consumption levels; this resulted from a negligible transformation of the energy mix (largely determined by fossil fuels). Generally, CO2 emissions declined over the study period at a rate comparable (−0.9%) to that of the reduction in energy consumption (−1.03%). In light of the LMDI method, the reduction in CO2 emissions from fuel consumption in the Polish agricultural sector was mainly driven by a reduction in energy intensity and in employment. Conversely, rapid growth in labor productivity was the key factor in increasing carbon dioxide emissions. Compared to these impacts, changes in other factors (i.e., emission intensity, energy mix and penetration of renewable energies) had an extremely small or marginal effect on the variation in CO2 emissions.

Carbon dioxide emissions intensity of Portuguese industry and energy sectors: A convergence analysis and econometric approach

The effects of carbon dioxide, methane and nitrous oxide emission taxes: An empirical study in China

Decoupling economic growth from carbon dioxide emissions in China: A sectoral factor decomposition analysis