Potential impacts of industrial structure on energy consumption and CO2 emission: a case study of Beijing

An optimization model is developed based on the Input–Output model to assess the potential impacts of industrial structure on the energy consumption and CO2 emission. The method is applied to a case study of industrial structure adjustment in Beijing, China. Results demonstrate that industrial structure adjustment has great potential of energy conservation and carbon reduction. When the average annual growth rate of GDP is 8.29% from 2010 to 2020, industrial structure adjustment can save energy by 39.42% (50.06 million tons of standard coal equivalent), and reduce CO2 emission by 46.06% (96.31 million tons) in Beijing in 2020. Second, Beijing had better strive to develop several low energy intensive and low carbon intensive sectors, such as information transmission, computer service and software, and finance. Third, energy intensity is possible to decrease without negatively affecting economic growth by reasonable industrial structure adjustment. Four, compared to “intensity targets”, “total amount targets” are more effective on the energy conservation and carbon reduction, but have much greater negative effects on economic growth. Therefore, it needs to be balanced between “total amount targets” and “intensity targets”

Summertime surface energy balance fluxes at two Beijing sites

Summertime (June to August 2015) radiative and turbulent heat fluxes were measured concurrently at two sites (urban and suburban) in Beijing. The urban site has slightly lower incoming and outgoing shortwave radiation, lower atmospheric transmissivity and a lower surface albedo compared to the suburban site. Both sites receive similar incoming longwave radiation. Although the suburban site had larger daytime outgoing longwave radiation (L_↑), differences in the daily mean L_↑ values are small, as the urban site has higher nocturnal L_↑. Overall, both the midday and daily mean net all-wave radiation (Q^*) for the two sites are nearly equal. However, there are significant differences between the sites in the surface energy partitioning. The urban site has smaller turbulent sensible heat (Q_H) (21-25% of Q^* (midday – daily)) and latent heat (Q_E) fluxes (21-45% of Q^*). Whereas, the suburban proportions of Q^* are Q_H 32-32% and Q_E 39-66%. The daily (midday) mean Bowen ratio (Q_H⁄Q_E ) was 0.56 and 0.49 (0.98 and 0.83) for the urban and suburban sites, respectively. These values are low compared to other urban and suburban areas with similar or larger fractions of vegetated cover. Likely these are caused by the widespread external water use for road cleaning/wetting, greenbelts, and air conditioners. Our suburban site has quite different land cover to most previous suburban studies as crop irrigation supplements rainfall. These results are important in enhancing our understanding of surface–atmosphere energy exchanges in Chinese cities, and can aid the development and evaluation of urban climate models and inform urban planning strategies in the context of rapid global urbanization and climate change

Carbon emissions from fossil fuel consumption of Beijing in 2012

The present study analyzed the consumption-based carbon emissions from fossil fuel consumption of Beijing in 2012. The multi-scale input–output analysis method was applied. It is capable of tracing the carbon emissions embodied in imports based on a global multi-regional input–output analysis using Eora data. The results show that the consumption-based carbon emission of Beijing has increased by 18% since 2007, which is 2.57 times higher than the production-based carbon emission in 2012. Only approximately 1/10 of the total carbon emissions embodied in Beijing's local final demand originated from local direct carbon emissions. Meanwhile, more than 4/5 were from domestically imported products. The carbon emission nexus between Beijing and other Chinese regions has become closer since 2007, while the imbalance as the carbon emission transfer from Beijing to other regions has been mitigated. Instead, Beijing has imported more carbon emissions from foreign countries. Some carbon emission reduction strategies for Beijing concerning different goals are presented on the basis of detailed discussion

Environment-economy tradeoff for Beijing–Tianjin–Hebei’s exports

The trade of goods among regions or nations associated with large environmental consequences. Yet balancing economic gains and environmental consequences induced by trade is still hindered by a lack of quantification of these two factors, especially for the environmental problems those are more locally oriented, such as the atmospheric pollution. Based on an environmental input-output analysis for 2010, we contrast economic gains (value added) against atmospheric pollutant emissions (sulfur dioxide (SO2), nitric oxide (NOx), primary fine particulate matter (PM2.5) and non-methane volatile organic compounds (NMVOC)) and the widely concerned CO2 emissions associated with international and interprovincial exports from Beijing-Tianjin-Hebei (BTH), the most polluted area in China. Our results show that exports contributed 55-62% of BTH's production emissions and 54% of its total value added. BTH's large exports of metals and metal products, nonmetal mineral products, chemical and transportation and warehousing, generated a larger share of pollutant emissions (36-46% of BTH's total) than that of value added (17%) along the supply chain. Most of BTH's embodied emissions in exports go to neighboring provinces and the developed east coastal regions in China, although the economic returns are comparatively low. Among BTH, industrial production in Beijing and Tianjin lead to more pollutant emission than value added in Hebei, due to reliance on pollution-intensive product imports from Hebei. Our results call for refocusing and restructuring of BTH's industry and trade structures to balance the economic gains and environmental losses for each region

Relationships between consecutive long-term and mid-term mobility decisions over the life course: a bayesian network approach

Long-term and mid-term mobility decision processes in different life trajectories generate complex dynamics, in which consecutive life events are interrelated and time dependent. This study uses the Bayesian network approach to study the dynamic relationships among residential events, household structure events, employment/education events, and car ownership events. Using retrospective data obtained from a web-based survey in Beijing, China, first structure learning is used to discover the direct and indirect relationships between these mobility decisions. Parameter learning is then applied to describe the conditional probabilities and predict the direct and indirect effects of actions and policies in the resulting network. The results confirm the interdependencies between these long-term and mid-term mobility decisions, and evidence the reactive and proactive behavior of individuals and households in the context of various life events over the course of their lives. In this regard, it is important to note that an increase in household size has a contemporaneous effect on car acquisition in the future; while residential events have a synergic relationship with employment/education events. Moreover, if people’s residential location or workplace/study location will move from an urban district to a suburban or outer suburban district, it has both lagged and concurrent effects on car acquisition

Transport emissions in Beijing: A scenario planning approach

This paper explores and analyses how to reduce smog-related air pollutants and carbon dioxide emissions generated by passenger transport systems in Beijing. In-depth surveys with experts and practitioners in China are used to examine the current business-as-usual projection for emissions in Beijing, the drivers and trends affecting current projections, and to develop alternative scenarios that might help reduce projected emissions significantly. These are based around different variants of population and migration growth and environmental stewardship. Current levels of smog caused by transport emissions are much higher in Beijing than internationally accepted safety standards, partly because of high levels of motorised traffic. Carbon dioxide emissions always tend to be overlooked because economic growth is prioritised. The sustainable model represents one of the best models for Beijing to follow; however, Beijing faces major challenges in becoming more environmentally sustainable over the next few years, mainly due to population growth and increased migration, even if there is powerful top-down government environmental stewardship. The aspiration to reduce smog-related air pollutants and carbon dioxide emissions in Beijing by implementing sustainable transport mitigation measures seems very ambitious; however, it is perhaps in this context that the real innovations in transport planning will emerge

Socioeconomic differences in adolescents’ smoking: a comparison between Finland and Beijing, China

Background: Various studies have demonstrated the associations between socioeconomic status (SES) and health and health behaviour among adolescents. However, few studies have compared the socioeconomic difference in adolescent smoking between countries with different stage of smoking. The purpose of this study was to examine and compare the relationship between socioeconomic status (SES) and adolescent smoking in Beijing, China and Finland through the Health Behaviour in School-aged Children (HBSC) study. Methods: The data used in this study were derived from the Chinese HBSC linked project survey 2008 in Beijing and the Finnish HBSC survey 2006. The final sample included 2005 Chinese and 1685 Finnish 15-year-old schoolchildren. The associations between Family Affluence Scale (FAS), as the SES measure, and adolescents’ smoking behaviour, including ever smoked, weekly smoking and the early onset of smoking were examined separately in two countries through binary logistic regression. Results: Compared to students from the high FAS group, Chinese boys from the low FAS group were more likely to report having ever smoked (OR = 2.12, 95 % CI = 1.49–3.01) and being early onset of smoking (OR = 2.17, 95 % CI = 1. 44–3.26). Finnish girls from the low FAS group were more likely to report being weekly smokers (OR = 1.68, 95 % CI = 1. 07–2.65). No significant difference was found for Chinese girls and Finnish boys. Conclusions: This study indicated different patterns of socioeconomic difference in smoking between Chinese and Finnish adolescents by gender and by smoking behaviour, which suggests that socioeconomic inequalities in smoking are different among adolescents in countries with different stage of smoking. Country specific policies and interventions for different target groups should be encouraged and designed for reducing the prevalence of adolescents’ smoking.peerReviewe