出版文献

Due to spatial heterogeneity worldwide, results from studies examining the effect of residential self-selection on travel behavior vary substantially. As a result of housing reform, the unique housing allocation system in China is a prime example of a context where the self-selection effect may conflict with international knowledge. Using a sample of 3836 residents, whom are living in Transit-Oriented Development (TOD) and non-TOD neighborhoods in Shanghai, this study untangles the effects that the built environment and residential self-selection have on travel behavior, in the context of diversified housing types in urban China. Specifically, this paper employs propensity score matching (PSM) to quantitate the relative importance of the built environment itself, verses residential self-selection, in influencing travel behavior for each of the housing types. The results show that the residential self-selection effect in the four types of housing (work-unit, commodity, public, and replacement) accounts for 15.2%, 30.7%, 18.5%, and 5.9% of the total impact on vehicle kilometers traveled (VKT), respectively. These findings expand the international database of point estimates in the relative contribution of self-selection toward the impact on travel behavior across global contexts, providing a comprehensive framework for similar studies on self-selection in other parts of the world.

Severe wintertime PM2.5 pollution in Beijing has been receiving increasing worldwide attention, yet the decadal variations remain relatively unexplored. Combining field measurements and model simulations, we quantified the relative influences of anthropogenic emissions and meteorological conditions on PM2.5 concentrations in Beijing over the winters of 2002–2016. Between the winters of 2011 and 2016, stringent emission control measures resulted in a 21 % decrease in mean mass concentrations of PM2.5 in Beijing, with 7 fewer haze days per winter on average. Given the overestimation of PM2.5 by the model, the effectiveness of stringent emission control measures might have been slightly overstated. With fixed emissions, meteorological conditions over the study period would have led to an increase in haze in Beijing, but the strict emission control measures have suppressed the unfavorable influences of the recent climate. The unfavorable meteorological conditions are attributed to the weakening of the East Asia winter monsoon associated particularly with an increase in pressure associated with the Aleutian Low.

Hydroxymethanesulfonate (HMS) has recently been identified as an abundant organosulfur compound in aerosols during winter haze episodes in northern China. It has also been detected in other regions although the concentrations are low. Because of the sparse field measurements, the global significance of HMS and its spatial and seasonal patterns remain unclear. Here, we modify and add to the implementation of HMS chemistry in the GEOS-Chem chemical transport model and conduct multiple global simulations. The model accounts for cloud entrainment and gas–aqueous mass transfer within the rate expressions for heterogeneous sulfur chemistry. Our simulations can generally reproduce quantitative HMS observations from Beijing and show that East Asia has the highest HMS concentration, followed by Europe and North America. The simulated HMS shows a seasonal pattern with higher values in the colder period. Photochemical oxidizing capacity affects the competition of formaldehyde with oxidants (such as ozone and hydrogen peroxide) for sulfur dioxide and is a key factor influencing the seasonality of HMS. The highest average HMS concentration (1–3 µg m−3) and HMS ∕ sulfate molar ratio (0.1–0.2) are found in northern China in winter. The simulations suggest that aqueous clouds act as the major medium for HMS chemistry while aerosol liquid water may play a role if its rate constant for HMS formation is greatly enhanced compared to cloud water.

Approximately seven million population in the Qinghai-Tibet Plateau of China, a global climate sensitive region, still rely primarily on yak dung for household cooking and heating. The treatment and combustion of yak dung result in a variety of negative impacts in terms of local alpine grassland degradation, indoor air pollution, public health risk, as well as global climate change. There is an urgent need to explore alternative pathway for affordable and clean energy as indicated in the United Nations’ Sustainable Development Goals for 2030. This perspective has analyzed the key challenges rooted in yak dung use on the Qinghai-Tibet Plateau region. Based on this, this perspective has further proposed a new complementary energy system to take advantage of locally available, clean and sustainable energy sources of wind and solar power, and have provided economic analyses. Meanwhile, this perspective has pointed out the potential barriers to promoting the new complementary energy system in the Qinghai-Tibet Plateau region due to traditional habits, economic factors and policies. Finally, strategies for transitioning from yak dung to the proposed alternative energy system is discussed at the end. Successful energy transition for the Qinghai-Tibet Plateau region offers an important option to achieving many other sustainable development goals related to climate change, economic development, and environment. The perspective is expected to shed light on the development of sustainable energy in other developing region or countries in the world to address multiple societal goals.

Urban green and open space are important components of achieving the goal of planning sustainable cities, by offering health benefits to urban dwellers and providing socio-economic and environmental benefits to society. Recent literature studied the usage of urban parks, however, few has addressed seasonal fluctuations of park visitor volume, let alone seasonal variations of home-park travel distances and park service areas. This paper not only empirically shows the seasonal variations of park visits but also examines links between the park visit patterns and spatial characteristics of the case parks. Applying spatial analysis methods to location data of over 1 million anonymous mobile phone samples collected from January to December 2011, we analyzed the seasonal variations in six medium-sized urban parks, of which size falls under the category of ‘district parks,’ in central Tokyo. We also conducted content analysis of a Japanese place review website to understand visitor perceptions of the case parks. On the other hand, park spatial characteristics data were collected and summarized through various ways including field observation and satellite image analysis. The results show that (1) while notable seasonal variations of park visitor volume and park service area existed in all case parks, the degree of variation also differed from park to park; (2) spatial characteristics of parks were closely interlinked to seasonal cultural events, to visitor perceptions, and consequently to seasonal fluctuations of the park visit patterns. Lessons learned from the policy perspective include highly diverse user groups visit these medium-sized urban parks than what the typical guidelines assume, and seasonal patterns of their visits considerably vary from park to park, interacting with spatial characteristics of the parks. Hence, the urban park planning process should consider specific and detailed characteristics of parks and allocate resources to respond to dynamic park visit patterns beyond generic guidelines.

The South Asian summer monsoon supplies over 80 % of India's precipitation. Industrialization over the past few decades has resulted in severe aerosol pollution in India. Understanding monsoonal sensitivity to aerosol emissions in general circulation models (GCMs) could improve predictability of observed future precipitation changes. The aims here are (1) to assess the role of aerosols in India's monsoon precipitation and (2) to determine the roles of local and regional emissions. For (1), we study the Precipitation Driver Response Model Intercomparison Project experiments. We find that the precipitation response to changes in black carbon is highly uncertain with a large intermodel spread due in part to model differences in simulating changes in cloud vertical profiles. Effects from sulfate are clearer; increased sulfate reduces Indian precipitation, a consistency through all of the models studied here. For (2), we study bespoke simulations, with reduced Chinese and/or Indian emissions in three GCMs. A significant increase in precipitation (up to ∼20 %) is found only when both countries' sulfur emissions are regulated, which has been driven in large part by dynamic shifts in the location of convective regions in India. These changes have the potential to restore a portion of the precipitation losses induced by sulfate forcing over the last few decades.

Governments foster voluntary actions within households to mitigate climate change. However, the literature suggests that they may not be as effective as expected due to rebound effects. We use a dynamic economy–energy–environment computable general equilibrium (CGE) model of the Catalan economy to simulate the effect of 75 different actions on GDP and net CO2 emissions, over a 20-year period. We also examine how a carbon tax could counteract the carbon rebound effects. We find energy rebound effects ranging from 61.77% to 117.49% for voluntary energy conservation actions, depending on where the spending is redirected, with similar carbon rebound values. In our main scenarios, where energy savings are redirected to savings and all non-energy goods proportionally, the rebound is between 64.47% and 66.90%. We also find, for these scenarios, that a carbon tax of between 2.4 and 3.6 €/ton per percentage point of voluntary energy reduction would totally offset carbon rebound effects. These results suggest that voluntary actions in households need additional measures to provide the expected results in terms of energy use reduction and climate change mitigation.

 

Introduction

An increasing number of walking studies discussed the relationship of walking with attitudes and perceptions. However, the findings were not consistent, and few studies examined the relationship between walking and attitudes to overall mobility and multiple modes. In this paper, we contribute to the debates by exploring the relationship between walking for transport and broad attitudes to urban mobility and transport modes.

Methods

Using a clustered random sample survey conducted in a second-tier city in China (N=1,048), we hypothesized that people with different attitudes have different amounts of walking for transport. Data analysis methods involved descriptive statistics, t-tests, Analysis of Variance (ANOVA), hierarchical logistic models, and hierarchical linear models.

Results

Positive attitudes and perceptions regarding multiple transport modes and related environments were associated with some walking for transport. T-tests indicated that those with different attitudes walked different amounts. Regression models showed that associations between attitudes and odds of people walking varied between genders. Males who perceived bus frequency was not a problem were more likely to walk. Females tended to walk when viewing transportation in the city as convenient. Both findings contribute to the understanding that positive perceptions of overall mobility in the city were associated with higher odds of walking. Meanwhile, among those who did walk, those with positive attitudes towards pedestrian safety crossing streets and those perceiving traffic jams as a problem in their daily trips spent more time walking.

Conclusion

This paper concludes that positive broad attitudes and perceptions of overall mobility and all transport modes are related to more walking activities. A better understanding of such relationships can provide a reference point for urban policies aiming at promoting walking for transport.

 

A striking feature of urban formation has been the deployment of mega-blocks, often on the order of sixteen hectares or more. On the other hand, recent urban policies give strong suggestions for smaller and finer-grained neighborhood block and grid arrangements. This paper explores the transformation of urban block structures in high-density cities beyond spatial conditions of big versus small blocks by emphasizing “place” making through the degree of spatial diversity and flexibility. Using spatial indices of urban block arrangements, road network efficiencies and gradients of transit network accessibility, the assessment on urban neighborhood block structure is applied to territories of central core, suburban and peripheral development in Beijing, Shanghai and Shenzhen at multiple spatial scales. The results show that the overall efficiency and flexibility of urban block structures becomes more a matter of a narrowing of the range of differing block sizes among the three territories and a concomitant higher potential capacity for adaptation to a broader range of development options. Beyond the Chinese context, in high-density cities across the globe, policies on place making should adopt a multi-scale spatial analysis strategy to measure the configuration of the overall urban block structure and guide the transformation of the city.

Objectives: This paper explores how the building-scale built environment is associated with self-rated health, examining differences in this association among younger, middle-aged, and older age groups. Features examined included building type, building condition, and sidewalk presence in front of dwellings.
Background: Understanding how the relationships between built environments and health vary across age groups helps to build a healthy environment for all. However, most studies have concentrated on the neighborhood or indoor environment, rather than whole buildings, and few have compared age groups.
Methods: This study analyzed survey data from 1,019 adults living in 40 neighborhoods in Chengdu, China, recruited through a clustered random sampling approach. It used a Bayesian logistic mixed effects model with interaction terms between age group indicators and other variables.
Results: Significant differences exist in the relationships of self-rated health with some environmental and other indicators among age groups. For older people, living in multi-floor buildings, having a household smoker, and undertaking fewer hours of weekly exercise were associated with lower odds of reporting good, very good, or excellent health. These relationships were not identified among middle-aged and younger people. More education was associated with higher odds of reporting better health among older and middle-aged groups.
Conclusions: Older people experience more health-related challenges compared to middle-aged and younger people. However, among the examined built environmental factors, building type was the only significant factor related to self-rated health among older people. To promote health among older people, this study recommends adding elevators in the multi-floor buildings.

Facing the dual challenges of climate change and air pollution, China has made great efforts to explore the co-control strategies for the both. We assessed the benefits of carbon and pollution control policies on air quality and human health, with an integrated framework combining an energy-economic model, an air quality model and a concentration–response model. With a base year 2015, seven combined scenarios were developed for 2030 based on three energy scenarios and three end-of-pipe control ones. Policy-specific benefits were then evaluated, indicated by the reduced emissions, surface concentrations of major pollutants, and premature deaths between scenarios. Compared to the 2030 baseline scenario, the nationwide PM2.5- and O3-related mortality was expected to decline 23% or 289 (95% confidence interval: 220–360) thousand in the most stringent scenario, and three quarters of the avoided deaths were attributed to the end-of-pipe control measures. Provinces in heavily polluted and densely populated regions would benefit more from carbon and pollution control strategies. The population fractions with PM2.5 exposure under the national air quality standard (35 μg/m3) and WHO guideline (10 μg/m3) would be doubled from 2015 to 2030 (the most stringent scenario), while still very few people would live in areas with the WHO guideline achieved for O3 (100 μg/m3). Increased health impact of O3 suggested a great significance of joint control of PM2.5 and O3 in future policy-making.
 

Hydrogen can play a key role in facilitating the transition to a future deeply decarbonized energy system and can help accommodate higher penetrations of renewables in the power system. Arguments to justify this conclusion are supported by an analysis based on real-world data from China’s Western Inner Mongolia (WIM). The economic feasibility and decarbonization potential of renewable-based hydrogen production are discussed through an integrated power-hydrogen-emission analytical framework. The framework combines a high-resolution wind resource analysis with hourly simulation for the operation of power systems and hydrogen production considering technical and economic specifications on selection of three different types of electrolyzers and two operating modes. The results indicate that using wind power to produce hydrogen could provide a cost-competitive alternative (<2 $kg-1) to WIM’s current coal-dominated hydrogen manufacturing system, contributing at the same time to important reductions in wind curtailment and CO2 emissions. The levelized cost for hydrogen production is projected to decrease in the coming decade consistent with increases in wind power capacity and decreases in capital costs for electrolyzers. Lessons learned from the study can be applied to other regions and countries to explore possibilities for larger scale economically justified and carbon saving hydrogen production with renewables.

Deep decarbonization pathways (DDPs) can be cost-effective for carbon mitigation, but they also have environmental co-benefits and economic impacts that cannot be ignored. Despite many empirical studies on the co-benefits of NDCs at the national or sectoral level, there is lack of integrated assessment on DDPs for their energy, economic, and environmental impact. This is due to the limitations of bottom-up and top-down models when used alone. This paper aims to fill this gap and link the bottom-up MAPLE model with a top-down CGE model to evaluate China's DDPs' comprehensive impacts. First, results show that carbon dioxide emissions can be observed to peak in or before 2030, and non-fossil energy consumption in 2030 is around 27%, which is well above the NDC target of 20%. Second, significant environmental co-benefits can be expected: 7.1 million tons of SO2, 3.96 million tons of NOx, and 1.02 million tons of PM2.5 will be reduced in the DDP scenario compared to the reference scenario. The health co-benefits demonstrated with the model-linking approach is around 678 billion RMB, and we observe that the linked model results are more in accordance with the conclusions of existing studies. Third, after linking, we find the real GDP loss from deep decarbonization is reduced from 0.92% to 0.54% in 2030. If the environmental co-benefits are considered, the GDP loss is further offset by 0.39%. The primary innovation of this study is to give a full picture of DDPs' impact, considering both environmental co-benefits and economic losses. We aim to provide positive evidence that developing countries can achieve targets higher than stated in the NDCs through DDP efforts, which will have clear environmental co-benefits to offset the economic losses.

We conduct a multi-model comparison of a carbon tax policy in China to examine how different models simulate the impacts in both near-term 2020, medium-term 2030, and distant future 2050. Though Top-down computable general equilibrium(CGE) models have been applied frequently on climate or other environmental/energy policies to assess emission reduction, energy use and economy-wide general equilibrium outcomes in China, the results often vary greatly across models, making it challenging to derive policies. We compare 8 China CGE models with different characteristics to examine how they estimate the effects of a plausible range of carbon tax scenarios – low, medium and high carbon taxes.. To make them comparable we impose the same population growth, the same GDP growth path and world energy price shocks. We find that the 2030 NDC target for China are easily met in all models, but the 2060 carbon neutrality goal cannot be achieved even with our highest carbon tax rates. Through this carbon tax comparison, we find all 8 CGE models differ substantially in terms of impacts on the macroeconomy, aggregate prices, energy use and carbon reductions, as well as industry level output and price effects. We discuss the reasons for the divergent simulation results including differences in model structure, substitution parameters, baseline renewable penetration and methods of revenue recycling.

Intra-city Public Charging Stations (PCSs) play a crucial role in promoting the mass deployment of Electric Vehicles (EVs). To motivate the investment on PCSs, this work proposes a novel framework to find the optimal location and size of PCSs, which can maximize the benefit of the investment. The impacts of charging behaviors and urban land uses on the income of PCSs are taken into account. An agent-based trip chain model is used to represent the travel and charging patterns of EV owners. A cell-based geographic partition method based on Geographic Information System is employed to reflect the influence of land use on the dynamic and stochastic nature of EV charging behaviors. Based on the distributed charging demand, the optimal location and size of PCSs are determined by mixed-integer linear programming. Västerås, a Swedish city, is used as a case study to demonstrate the model's effectiveness. It is found that the charging demand served by a PCS is critical to its profitability, which is greatly affected by the charging behavior of drivers, the location and the service range of PCS. Moreover, charging price is another significant factor impacting profitability, and consequently the competitiveness of slow and fast PCSs.

As more countries commit to emissions reductions by midcentury to curb anthropogenic climate change, decarbonization of the electricity sector becomes a first-order task in reaching this goal. Renewables, particularly wind and solar power, will be predominant components of this transition. How availability of the wind and solar resource will change in the future in response to regional climate changes is an important and underdiscussed topic of the decarbonization process. Here, we study changes in potential for wind power in China and India, evaluating prospectively until the year 2060. To do this, we study a downscaled, high-resolution multimodel ensemble of CMIP5 models under high and low emissions scenarios. While there is some intermodel variability, we find that spatial changes are generally consistent across models, with decreases of up to 965 (a 1% change) and 186 TWh (a 2% change) in annual electricity generation potential for China and India, respectively. Compensating for the declining resource are weakened seasonal and diurnal variabilities, allowing for easier large-scale wind power integration. We conclude that while the ensemble indicates available wind resource over China and India will decline slightly in the future, there remains enormous potential for significant wind power expansion, which must play a major role in carbon neutral aspirations.

Deployment of negative emission technologies needs to start immediately if we are to avoid overshooting international carbon targets, reduce negative climate impacts, and minimize costs of emission mitigation. Actions in China, given its importance for the global anthropogenic carbon budget, can be decisive. While bioenergy with carbon capture and storage (BECCS) may need years to mature, this study focuses on developing a ready-to-implement biomass intermediate pyrolysis poly-generation (BIPP) technology to produce a potentially stable form of biochar, a medium for carbon storage, and to provide a significant source of valuable biofuels, especially pyrolysis gas. Combining the experimental data with hybrid models, the results show that a BIPP system can be profitable without subsidies: its national deployment could contribute to a 68% reduction of carbon emissions per unit of GDP in 2030 compared to 2005 and could result additionally in a reduction in air pollutant emissions. With 73% of national crop residues converted to biochar and other biofuels in the near term (2020 to 2030), the cumulative greenhouse gas (GHG) reduction could reach up to 5653 Mt CO₂-eq by 2050, which could contribute 9-20% of the global GHG emission reduction goal for BECCS (28-65 Gt CO₂-eq in IPCC’s 1.5 °C pathway), and nearly 2633 Mt more than that projected for BECCS alone. The national BIPP development strategy is developed on a provincial scale based on a regional economic and life-cycle analysis. 

With the development of deregulated retail power markets, it is possible for end users equipped with smart meters and controllers to optimize their consumption cost portfolios by choosing various pricing plans from different retail electricity companies. This paper proposes a reinforcement learning-based decision system for assisting the selection of electricity pricing plans, which can minimize the electricity payment and consumption dissatisfaction for individual smart grid end user. The decision problem is modeled as a transition probability-free Markov decision process (MDP) with improved state framework. The proposed problem is solved using a Kernel approximator-integrated batch Q-learning algorithm, where some modifications of sampling and data representation are made to improve the computational and prediction performance. The proposed algorithm can extract the hidden features behind the time-varying pricing plans from a continuous high-dimensional state space. Case studies are based on data from real-world historical pricing plans and the optimal decision policy is learned without a priori information about the market environment. Results of several experiments demonstrate that the proposed decision model can construct a precise predictive policy for individual user, effectively reducing their cost and energy consumption dissatisfaction.