Effects of urbanization on freight transport carbon emissions in China: Common characteristics and regional disparity
TL;DR: Wang et al. as mentioned in this paper analyzed the driving factors of freight transport carbon emissions and the effects of urbanization on freight transport CO 2 emissions in China, and provided policy implications based on the findings, which are expected to contribute to the carbon emissions reduction in China's transportation industry.
About: This article is published in Journal of Cleaner Production.The article was published on 2019-02-20. It has received 99 citations till now. The article focuses on the topics: Greenhouse gas & Population.
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TL;DR: In this paper, the influence of foreign direct investment (FDI) inflows and income on the environmental sustainability has been investigated by several studies on China, as well as the rest of the world economies.
138 citations
TL;DR: This study integrated the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) stable nighttime light data and Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) composite data, and established the integrated nighttime light datasets from 1992 to 2016, and provided policy implications based on the results.
107 citations
TL;DR: Using data from 30 provinces of China's transport sector, this paper investigated and evaluated the regional differences of carbon emission intensity (CEI), and employed the extended STIRPAT (stochastic impacts by regression on population, affluence and technology) and GTWR (geographically and temporally weighted regression) model to reveal the influence of driving factors on CEI from spatial-temporal perspectives.
102 citations
TL;DR: In this article, the authors examined a positive relationship between perceived impact of road and transport infrastructure development and local community support for tourism through perceived environmental impact, perceived tourism benefits, and perceived community satisfaction.
101 citations
TL;DR: In this paper, the epsilon-based measure data envelopment analysis model with undesirable outputs is applied to estimate TSCDEE for 30 provinces in China from 2010 to 2016.
93 citations
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TL;DR: A technique is developed, termed geographically weighted regression, which attempts to capture variation by calibrating a multiple regression model which allows different relationships to exist at different points in space by using Monte Carlo methods.
Abstract: Spatial nonstationarity is a condition in which a simple “global” model cannot explain the relationships between some sets of variables. The nature of the model must alter over space to reflect the structure within the data. In this paper, a technique is developed, termed geographically weighted regression, which attempts to capture this variation by calibrating a multiple regression model which allows different relationships to exist at different points in space. This technique is loosely based on kernel regression. The method itself is introduced and related issues such as the choice of a spatial weighting function are discussed. Following this, a series of related statistical tests are considered which can be described generally as tests for spatial nonstationarity. Using Monte Carlo methods, techniques are proposed for investigating the null hypothesis that the data may be described by a global model rather than a non-stationary one and also for testing whether individual regression coefficients are stable over geographic space. These techniques are demonstrated on a data set from the 1991 U.K. census relating car ownership rates to social class and male unemployment. The paper concludes by discussing ways in which the technique can be extended.
2,330 citations
TL;DR: The general value of the IPAT model is confirmed as a starting point for understanding the anthropogenic driving forces of global change and it is suggested that population and economic growth anticipated over the next decade will exacerbate greenhouse gas emissions.
Abstract: We developed a stochastic version of the Impact = Population·Affluence·Technology (IPAT) model to estimate the effects of population, affluence, and technology on national CO2 emissions. Our results suggest that, for population, there are diseconomies of scale for the largest nations that are not consistent with the assumption of direct proportionality (log–linear effects) common to most previous research. In contrast, the effects of affluence on CO2 emissions appear to reach a maximum at about $10,000 in per- capita gross domestic product and to decline at higher levels of affluence. These results confirm the general value of the IPAT model as a starting point for understanding the anthropogenic driving forces of global change and suggest that population and economic growth anticipated over the next decade will exacerbate greenhouse gas emissions.
1,359 citations
TL;DR: In this paper, the authors explore the determinants of CO 2 emissions using the STIRPAT model and data from 1980 to 2011 for OECD countries, and find that non-renewable energy consumption increases CO2 emissions whereas renewable energy consumption decreases CO 2 emission.
764 citations
TL;DR: In this article, the authors investigated the relationship between urbanization and carbon emission in BRICS countries within the period 1985-2014 and found that in the long term, urbanization causes carbon emission.
174 citations