scholarly journals Impact of Population Aging on Carbon Emission in China: A Panel Data Analysis

2018 ◽  
Vol 10 (7) ◽  
pp. 2458 ◽  
Author(s):  
Weidong Li ◽  
Xin Qi ◽  
Xiaojun Zhao
Keyword(s):  
Panel Data ◽  
Carbon Emission ◽  
Population Aging ◽  
Panel Data Analysis ◽  
Negative Relationship ◽  
Modern Society ◽  
Eastern Region ◽  
Low Carbon ◽  
Stirpat Model ◽  
The Impact

The impact of population structure on carbon emission has always been a key area of research in modern society. In this paper, we propose a new expanded STIRPAT model and panel co-integration method to analyze the relationship between population aging and carbon emission, based on the provincial panel data in China from 1999 to 2014. Empirical results show that there exists a significant inverted U-shaped curve between the population aging and carbon emission. There also exist regional discrepancies, where the impact of the population aging on carbon emission in the eastern region is significantly positive. By contrast, a negative relationship arises in the central and western regions. Finally, several suggestions for low carbon development are provided.

scholarly journals An Aging Giant At The Center of Global Warming: Population Dynamics and Its Effect on CO2 Emissions in China

2022 ◽  
Author(s):  
Hongwei Guo ◽  
Jia Jiang ◽  
Yuanyuan Li ◽  
Mengqing Liu ◽  
Ji Han
Keyword(s):  
Sustainable Development ◽  
Regional Difference ◽  
Industrial Structure ◽  
Population Aging ◽  
Policy Implications ◽  
Eastern Region ◽  
Low Carbon ◽  
Stirpat Model ◽  
Effective Strategies ◽  
Complex Correlation

Abstract Managing the aging crisis and mitigating CO2 emissions are currently two great challenges faced by China. Revealing the complex correlation between aging and CO2, and projecting their future dynamics are fundamentally necessary to inform effective strategies and policies toward a low-carbon and sustainable development in China. In this paper, we quantitatively investigated the impacts of population aging, economy, and energy intensity on CO2 emissions through a STIRPAT model based on balanced provincial panel data from 1995-2019, and employed a cohort model and scenario analysis to project the demographic change and CO2 emissions till 2050. It is found that CO2 emissions in China has witnessed a significant growth during 1995-2019, and will exhibit an inverted U-shaped growth till 2050 with its peak appears between 2030-2040. Every 1% increase of aging will exert a 0.69% emission of CO2 in China. However, a big regional difference was also detected as aging contributed to CO2 reduction in the eastern region, but stimulated CO2 emissions in the central and western regions. Policy implications for achieving a low-carbon and aging-oriented sustainable development include the integration of aging into the decision-making of industrial structure upgrading and CO2 emission reduction in both national and region levels, the promotion of the further transition to low-carbon consumption and green products in the eastern region, and strengthening the deep fusion of aging-oriented industries with local resource and environmental endowment in the central and western regions such as the development of eco-agriculture and green pension industries.

scholarly journals Primary commodity export and economic growth in sub sahara africa: evidence from panel data analysis

2011 ◽  
Vol 11 (4) ◽  
pp. 465-474 ◽  
Author(s):  
Matthew Ocran ◽  
Nicholas Biekpe
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Fixed Effects ◽  
Panel Data Analysis ◽  
Negative Relationship ◽  
Policy Implications ◽  
Sub Saharan Africa ◽  
Primary Commodity ◽  
Commodity Export ◽  
The Impact

The paper sought to examine the impact of instability in primary commodity export earnings and the level of commodity dependence on economic growth in Sub Saharan Africa (SSA).  Fixed effects panel data estimator was used in the empirical estimation. The findings of the study suggest that there is a negative relationship between instability in export earnings and economic growth. The results also indicate that the level of commodity dependence matter in determining economic growth in the region. The results of the paper have economic development policy implications for SSA economies and these are not farfetched. First, it appears the difficult growth experience of SSA is not solely due to instability in export receipts. The question of continued dependence on a narrow range of primary commodities is also matter of great importance.

scholarly journals Spatio-Temporal Effects of Multi-Dimensional Urbanization on Carbon Emission Efficiency: Analysis Based on Panel Data of 283 Cities in China

2021 ◽  
Vol 18 (23) ◽  
pp. 12712
Author(s):  
Zhanhang Zhou ◽  
Linjian Cao ◽  
Kuokuo Zhao ◽  
Dongliang Li ◽  
Ci Ding
Keyword(s):  
Panel Data ◽  
Spatial Heterogeneity ◽  
Carbon Emission ◽  
Negative Impact ◽  
Low Carbon ◽  
Promoting Effect ◽  
Gradual Improvement ◽  
Emission Efficiency ◽  
Spatio Temporal ◽  
The Impact

Under the influence of complex urbanization, improving the carbon emission efficiency (CEE) plays an important role in the construction of low-carbon cities in China. Based on the panel data of 283 prefectural-level cities in China from 2005 to 2017, this study evaluated the CEE by the US-SBM model, and explored the spatial agglomeration evolution characteristics of CEE from static and dynamic perspectives by integrating ESDA and Spatial Markov Chains. Then, the spatial heterogeneity of the impacts of multi-dimensional urbanization on CEE were analyzed by using the Geographically and Temporally Weighted Regression (GTWR). The results show that: (1) with the evolution of time, the CEE has a trend of gradual improvement, but the average is 0.4693; (2) from the perspective of spatial static agglomeration, the “hot spots” of CEE mainly concentrated in Shandong Peninsula, Pearl River Delta, and Chengdu-Chongqing urban agglomeration; The dynamic evolution of CEE gradually forms the phenomenon of “club convergence”; (3) urbanization of different dimensions shows spatial heterogeneity to CEE. The impact of economic urbanization in northern cities on CEE shows an inverted “U” shape, and the negative impact of spatial urbanization on CEE appears in the northwest and resource-based cities around Bohai Sea. Population and social urbanization have a positive promoting effect on CEE after 2010. These findings may help China to improve the level of CEE at the city level and provide a reference for low-carbon decision-making.

scholarly journals The Role of Renewables in a Low-Carbon Society: Evidence from a Multivariate Panel Data Analysis at the EU Level

2019 ◽  
Vol 11 (19) ◽  
pp. 5260 ◽  
Author(s):  
Mihail Busu
Keyword(s):  
Panel Data ◽  
Population Growth ◽  
Co2 Emissions ◽  
Carbon Emission ◽  
Linear Regression Analysis ◽  
Biofuel Production ◽  
Total Carbon ◽  
Low Carbon ◽  
Eu Countries ◽  
The Impact

Low carbon emission has a major positive impact on our society. Due to the importance of reducing carbon emission levels, factors that contribute significantly towards reducing carbon emission levels have attracted the interest of academics and researchers in the field. In this paper, the author develops a multiple linear regression analysis to examine the relationship between renewable energy consumption, biofuel production, resources productivity, bioenergy productivity, the level of urbanization and population and their impact on total carbon dioxide (CO2) emissions. Data was collected from the European Statistical Office (EUROSTAT) and four statistical hypotheses were validated through a regression model with panel data using the statistical software EViews 11. The study was conducted for 27 European Union (EU) countries during 2008 to 2017. The author’s findings indicate that renewables have a direct and positive influence on the levels of CO2 emissions, as opposed to population growth and urbanization. These findings suggest that public policy should be directed towards increasing the use of renewables in EU countries, while the level of urbanization and the population growth add more restrictions in the modelling equation of the impact on CO2 emissions.

The Impact of Income Inequality on Carbon Emissions in Asian Countries: Non-Parametric Panel Data Analysis

2021 ◽  
Author(s):  
Tarek Ghazouani ◽  
Lamia Beldi
Keyword(s):  
Panel Data ◽  
Income Inequality ◽  
Panel Data Analysis ◽  
Negative Relationship ◽  
Estimation Method ◽  
Asian Countries ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
The Impact ◽  
Non Parametric

Abstract Widening income inequality and environmental degradation are two of the most important problems that threat the sustainable development. For that, this study aims to examine the effect of income distribution on CO2 emissions in seven Asian countries over the period 1971–2014 using a non-parametric panel estimation method and time-varying coefficients. Specifically, we apply the local linear dummy variable estimator (LLDVE) approach that allows evaluating the coefficients which vary over time for the panel data models. The results reveal that there is a strong non-linear correlation between income inequality and per capita CO2 emissions. The non parametric model suggest that there is a negative relationship between income inequality and environment degradation over the whole study period expect for the period 1988–1997 which was positive. Our findings broadly support the existence of the "equity-pollution dilemma", whereby income redistribution induces environmental pollution. This dilemma has potential implications for policies designed to promote redistribution in the selected Asian countries.

scholarly journals Population Aging, Health Investment and Economic Growth: Based on a Cross-Country Panel Data Analysis

2021 ◽  
Vol 18 (4) ◽  
pp. 1801
Author(s):  
Yingzhu Yang ◽  
Rong Zheng ◽  
Lexiang Zhao
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Population Aging ◽  
Panel Data Analysis ◽  
Health Investment ◽  
Investment Structure ◽  
Aging And Health ◽  
Cross Country ◽  
Aging Health ◽  
The Impact

With the economic development of various countries and the deepening of population aging, health plays an increasingly important role in the macro-economy. How to meet the growing health needs as well as promote the economy has captured the attention of the world. Therefore, whether health investment can promote economic growth is an important theoretical and practical issue. An extended Mankiw–Romer–Weil model (MRW) with human health capital and population aging is employed to examine the impact on economic growth from population aging and health investment. On the basis of the theoretical model, this paper uses the LSDV and TSLS methods to carry out an empirical study based on cross-country panel data during the period 2000–2016. The empirical results show that health investment plays a significant role in promoting economic growth, and there is an inverted U-shaped relationship between population aging and economic growth. The impacts on economic growth from health investment and population aging can weaken each other. In addition, this paper also finds that health investment structure and the proportion of government health investment to total government spending can affect economic growth.

Panel Data Analysis of Determinants of Leverage in the Automobile Industry in India

GIS Business ◽  
2018 ◽  
Vol 13 (2) ◽  
pp. 29-47
Author(s):  
Vibha Tripathi
Keyword(s):  
Panel Data ◽  
Capital Structure ◽  
Automobile Industry ◽  
Panel Data Analysis ◽  
Negative Relationship ◽  
Order Theory ◽  
Ordinary Least Squares ◽  
Pecking Order ◽  
Trade Off ◽  
Coverage Ratio

The study tries to investigate the key determinants of capital structure of leading automobile companies and the Automobile Industry in India. The study also tracks the theory implications, i.e. trade off vs. pecking order in these firms and the industry in general. An attempt is to see, if individually each sample company and the whole industry are influenced by the same determinants of capital structure. Pooled ordinary least squares and panel data econometric techniques such as fixed effect models are used to investigate the most significant determinants that affect the capital structure choice of 10 leading companies categorized as BSE Auto Top 100 and the Automobile Industry as a whole for a period of 14 years from 2000–2001 to 2013–2014. The study reveals some interesting facts and results. Multiple regression analysis reveals that while profitability and size are significant determinants in most of the leading companies; NDTS, Growth, and Debt service coverage ratio are not significant for these companies. While the Panel data results of the Automobile Industry as a whole reveals that profitability is the only significant determinant having negative relationship with debt equity ratio; and the other variables are insignificant. Also individual companies coefficient results shows implications of mix of pecking order and trade off theories while the panel data results of the whole Industry strongly supports the Pecking order theory.

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