Most of the existing carbon emission studies based on the IPAT framework considered the size effect rather than structure effect of population. However, it is proved with the micro-data household evidence that the demographic structure explains the unexpected trends better. To complete the framework, this study integrated the structure effects with the STIRPAT model base on the household life-cycle consumption theory as different age groups differ in carbon consumption behaviors. For further analysis with the frequent extreme weather events caused by global warming and their catastrophic effect on human activities, this study also harmonized Köppen criteria with the theories model by Syukuro Manabe and Klaus Hasselmann and considers climate factors precipitation (PRE), annual degree-day (DD), and temperature anomaly (TA) with the extended model to investigate whether population aging trend provides room for or creates barriers to carbon reduction. NASA night-time light (NTL) data DMSP/OLS and VIIRS/DNB is adopted as the proxy for population density to weight the relevant climate data from over 30,000 weather stations worldwide. The combined dataset is from 150 countries, and the period is during 1970–2013. The Panel Seemingly Unrelated Regression (SUR) method is used to solve the problems of cross-sectional correlation, non-stationarity, and endogeneity since sample countries are closely linked in the global meteorological system which make each cross-sectional disturbance term likely to be contemporaneously correlated, and endogeneity of carbon emission under the same global agreement constraint. The empirical results show that the age structure had significant and different impacts on carbon emissions. The general influence of age growth is an inverted U shape as the younger group consumes less than the older group, and offspring leave the family when the householder turns 50. The EKC theory is also checked with the threshold model of per capita income on carbon emissions to determine how many countries reached carbon peak. This study proved that the aggregated carbon consumption pattern is aligned with the microlevel evidence on household energy consumption. Another distinguished finding is that population aging may generally lead to an increase in heat and electricity carbon emissions, contrary to what some household energy consumption models would predict. We explain the uplifted tail as the “effect caused by the narrowed adaptation temperature range” when people are getting older and vulnerable. It should be noted that as the aging trend becomes severe worldwide and extreme weather events happen with higher frequency, the potential energy spending and thus carbon emission on air conditioning will undoubtfully overgrow. One important method is to improve the building energy efficiency by retrofitting old buildings’ insulations. Implementing new green building standards in carbon reduction must not be ignored. Evidence shows that if the insulation of pre-1990s houses is reconstructed with modern materials, carbon emissions caused by residential cooling and heating can be reduced by about 20% every year. Overall, promoting an efficient building style provides reduction capacity for the industrial sector, and it is a way to achieve sustainable growth.
The ten essays in Future Challenges of Cities in Asia engage with some of the most critical urban questions of the near future across Asia. These comprise socio-economic and cultural transitions as a result of urbanization; environmental challenges, especially questions of climate change, natural disasters, and environmental justice; and the challenges of urban infrastructure, built form, and new emerging types of urban settlements. The essays demonstrate that it is increasingly difficult to conceptualize the ‘urban’ as one particular type of settlement. Rather, it would be more accurate to say that the ‘urban’ characterizes a global transition in the way we are beginning to think about settlements. This book is of interest not only to researchers interested in comparative and inter-disciplinary research, but also to urban practitioners more broadly, illustrating through concrete cases the challenges that urban regions in Asia and beyond are facing, and the various opportunities that exist for dealing with these challenges.
In order to achieve economic growth without destroying the environment and consuming resources, it is necessary to reasonably predict the development trend of circular economy. The evolution path of circular economy in the coastal regions was analysed based on the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) method. The dynamic investigation was carried out through the variations of moving trajectory of model space points in a certain period of time, the evolution path of circular economy development in coastal regions was numerically simulated to conclude the evolution law of sustainable growth path in coastal regions, and the future development trend of coastal regional circular economy was predicted. Finally, the composite indicator is synthesized accordingly as an example to quantify the development of the Center of around Bohai Sea Economic Rim (BER). On the basis of introducing the basic situation of BER as well as its circular economy development status, comprehensive economic performance dataset of BER from 2005 to 2018 is used with specified TOPSIS method to improve the accuracy of evaluation. In addition, on the basis of the rule, the future development direction of the circular economy of the BER was predicted, and the policy suggestions were put forward in view of the development status and problems of circular economy.
The relationship between energy consumption and economic growth is a hot issue in today's society. This paper aims to empirically verify the relationship between energy consumption and economic growth. This article analyzes the relation of energy consumption with the economic growth taking the case of South Asian countries (Afghanistan, Bangladesh, Bhutan, India, Pakistan, Sri Lanka, and Nepal) along with the macroeconomic determinants that affect the total economic growth – FDI growth, CPI rate and population growth in order to avoid omitted variable bias and misleading results. The time span of this study covers the period of 1980–2019. To examine the significant relation of these determinants and impact of energy consumption on economic growth, In-pooled regression, Fixed-effects, Bidirectional fixed effect, Random-effects, and GLS estimation regression model are used. The estimated results show a positive correlation of energy consumption and all other economic determinants with economic growth except CPI, where there is a negative correlation founded.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.