Publications

Understanding how internal atmospheric variability affects Greenland Ice Sheet (GrIS) summertime melting would improve understanding of future sea level rise. We analyze the Community Earth System Model Large Ensemble (CESM‐LE) over 1951‐2000 and 2051‐2100. We find that internal variability dominates the forced response on short timescales (~20 years) and that the area impacted by internal variability grows in the future, connecting internal variability and climate change. Unlike prior studies, we do not assume specific patterns of internal variability to affect GrIS melting, but derive them from Maximum Covariance Analysis. We find that the North Atlantic Oscillation (NAO) is the major source of internal atmospheric variability associated with GrIS melt conditions in CESM‐LE and reanalysis, with the positive phase (NAO+) linked to widespread cooling over the ice sheet. CESM‐LE and CMIP5 project an increase in the frequency of NAO+ events, suggesting a negative feedback to the GrIS under future climate change.

This paper considers options for a future Indian power economy in which renewables, wind and solar, could meet 80% of anticipated 2040 power demand supplanting the country’s current reliance on coal. Using a cost optimization model, here we show that renewables could provide a source of power cheaper or at least competitive with what could be supplied using fossil-based alternatives. The ancillary advantage would be a significant reduction in India’s future power sector related emissions of CO₂. Using a model in which prices for wind turbines and solar PV systems are assumed to continue their current decreasing trend, we conclude that an investment in renewables at a level consistent with meeting 80% of projected 2040 power demand could result in a reduction of 85% in emissions of CO₂ relative to what might be expected if the power sector were to continue its current coal dominated trajectory.

Rapid urban expansion in China has created both opportunities and challenges for promoting active transport in urban residential communities. Previous studies have shown that the urban form at the city scale has affected active transport in Chinese cities. However, there is less agreement about how the physical and social variations of neighborhood types should be addressed. This research investigates the four most representative neighborhood types found in Chinese cities: traditional mixed-use, slab block work-unit, gated community, and resettlement housing. Household travel diaries conducted in Chengdu in 2016 were analyzed using binary logistic regressions, supplemented by informal onsite interviews. The findings indicate significant variations in the use and accessibility of active transport in each neighborhood type for non-work trips. This suggests that each neighborhood type may need different strategies for promoting active transport: (1) the traditional mixed-use neighborhoods are in need of intensified urban retrofitting projects to reclaim public open space; (2) the work-unit could benefit from comprehensive plans rather than a patchwork of projects; (3) while opening up gated communities can improve porosity across neighborhoods and promote active transport, the more pressing issue may be their inability to keep up with the transportation needs of the residents; and (4) residents of resettlement housing should have better access to employment using transit and non-motorized modes.

In China a centralized planning culture has created similar neighborhoods across the country. Using a survey of 1,048 individuals conducted in 2016 in Chengdu—located in a carefully conceptualized typology of neighborhood forms—we analyzed the associations between individual and neighborhood characteristics and active or non-motorized transport behavior. Using several multiple logistic and multi-level models, we show how neighborhoods were categorized and the number of categories or neighborhood types affected the magnitude of the associations with active transport but not the direction. People taking non-work trips were more likely to use active compared with motorized modes in all neighborhood types. Neighborhood type was significant in models, but so were many other individual-level variables and infrastructural and locational features such as bike lanes and location near the river. Of the 3-D physical environment variables, floor area ratio (a proxy for density) was only significant in one model for non-work trips. Intersection density and dissimilarity (land use diversity) were only significant in a model for work trips. This study shows that to develop strong theories about the connections between active transport and environments, it is important to examine different physical and cultural contexts and perform sensitivity analyses. Research in different parts of China can help provide a more substantial base for evidence-informed policy-making. Planning and design recommendations related to active transport need to consider how neighborhoods, built environments, and personal characteristics interact in different kinds of urban environments.

As part of the climate policy to meet the 2‐degrees Celsius (2 °C) target, actions in all economic sectors, including agriculture, are required to mitigate global greenhouse gas (GHG) emissions. While there has been an ever‐increasing focus on agricultural greenhouse gas (AGHG) emissions, limited attention has been paid to their economic drivers in the globalized world economy and related mitigation potentials. This paper makes a first attempt to trace AGHG emissions via global trade networks using a multi‐regional input‐output model and a complex network model. Over one third of global AGHG emissions in 2012 can be linked with products traded internationally, of which intermediate trade and final trade contribute 64.2% and 35.8%, respectively. Japan, the USA, Germany, the UK, and Hong Kong are the world's five largest net importers of embodied emissions, while Ethiopia, Australia, Pakistan, India and Argentina are the five largest net exporters. Some hunger‐afflicted developing countries in Asia and Africa are important embodied emission exporters, due to their large‐scale exports of agricultural products. Trade‐related virtual AGHG emission transfers shape a highly heterogenous network, due to the coexistence of numerous peripheral economies and a few highly‐connected hub economies. The network clustering structure is revealed by the regional integration of several trading communities, while hub economies are collectors and distributors in the global trade network, with important implications for emission mitigation. Achieving AGHG emission reduction calls for a combination of supply‐ and demand‐side policies covering the global trade network.

Modelling and projecting consumption, investment and government demand by detailed commodities in CGE models poses many data and methodological challenges. We review the state of knowledge of modelling consumption of commodities (price and income elasticities and demographics), as well as the historical trends that we should be able to explain. We then discuss the current approaches taken in CGE models to project the trends in demand at various levels of commodity disaggregation. We examine the pros and cons of the various approaches to adjust parameters over time or using functions of time and suggest a research agenda to improve modelling and projection. We compare projections out to 2050 using LES, CES and AIDADS functions in the same CGE model to illustrate the size of the differences. In addition, we briefly discuss the allocation of total investment and government demand to individual commodities.

Big data is increasingly regarded as a new approach for understanding urban informatics and complex systems. Today, there is unprecedented data availability, with detailed remote-sensed data on the built environment and rich mineable web-based sources in the form of social media, web mapping, information services and other sources of unstructured "big data". 

This book brings together a group of international contributors to consider the geographical implications of mobility, wellbeing and development within and across Chinese cities through location-based big data perspectives. The degree of urban sprawl, productive density and vibrancy can be reflected from location-based social media big data. The challenge is to identify, map and model these relationships to develop cities at different places in the urban hierarchical system that are more sustainable. This edited book aims to tackle these issues through two inter-related geographical scales: inter-city level and intra-city level.

The text is designed for graduate courses in planning, geography, public policy and administration, and for international researchers who are involved in urban and regional economics and economic geography.

China has reduced growth in its emissions of greenhouse gases, success attributable in part due to major investments in onshore wind. By comparison, investments in offshore wind have been minor, limited until recently largely by perceptions of cost. Assimilated meteorological data are used here to assess future offshore wind potential for China. Analysis on a provincial basis indicates that the aggregate potential wind resource is 5.4 times larger than current coastal demand for power. Recent experiences with markets both in Europe and the US suggest that potential offshore resources in China could be exploited to cost-competitively provide 1148.3 TWh of energy in a high-cost scenario, 6383.4 TWh in a low-cost option, equivalent to between 36% and 200% of the total coastal energy demand post 2020. The analysis underscores significant benefits for offshore wind for China, with prospects for major reductions greenhouse emissions with ancillary benefits for air quality.

A regional fully coupled meteorology–chemistry model, Weather Research and Forecasting model with Chemistry (WRF-Chem), was employed to study the seasonality of ozone (O₃) pollution and its sources in both China and India. Observations and model results suggest that O₃ in the North China Plain (NCP), Yangtze River Delta (YRD), Pearl River Delta (PRD), and India exhibit distinctive seasonal features, which are linked to the influence of summer monsoons. Through a factor separation approach, we examined the sensitivity of O₃ to individual anthropogenic, biogenic, and biomass burning emissions. We found that summer O₃ formation in China is more sensitive to industrial and biogenic sources than to other source sectors, while the transportation and biogenic sources are more important in all seasons for India. Tagged simulations suggest that local sources play an important role in the formation of the summer O₃ peak in the NCP, but sources from Northwest China should not be neglected to control summer O₃ in the NCP. For the YRD region, prevailing winds and cleaner air from the ocean in summer lead to reduced transport from polluted regions, and the major source region in addition to local sources is Southeast China. For the PRD region, the upwind region is replaced by contributions from polluted PRD as autumn approaches, leading to an autumn peak. The major upwind regions in autumn for the PRD are YRD (11 %) and Southeast China (10 %). For India, sources in North India are more important than sources in the south. These analyses emphasize the relative importance of source sectors and regions as they change with seasons, providing important implications for O₃ control strategies.

Ecosystems at the margins of their zone could be amongst the first to experience significant shifts in structure and function. At this site there have already been signs of permafrost degradation and more frequent temperature and precipitation anomalies. The canopy-dominant larch accounted for half the total T fluxes. The remaining 50% was distributed evenly among intermediate and suppressed trees. T is the dominant subcomponent in ET, where overall T/ET varies of 66%–84% depending on precipitation patterns. In dormant and early growing seasons, T still constitutes a majority of ET even though the canopy foliage is not fully developed because cold soil creates a negative soil to air vapor pressure gradient that impedes evaporation. However, in the peak growing season, excess precipitation reduces T while providing sufficient wetness for surface evaporation. ET from standard data product based on MODIS satellite reflectance underestimates tower ET by 17%–29%. Solar-induced chlorophyll fluorescence measured by satellite is well correlated with tower ET (r² = 0.69–0.73) and could provide a better basis for regional ET extrapolations. Sites along boreal ecotones are critical to observe for signs of shifts in their structure, function, and response to climate anomalies.

In developing countries with a large population and fast urbanization, High-rise Residential Buildings (HRBs) have unavoidably become a very common, if not the most, accommodation solution. The paradigm of HRB energy consumption is characterized by high-density energy consumption, severe peak effects and a limited site area for integrating renewable energy, which constitute a hindrance to the low-carbon transition. This review paper investigates low-carbon transition efforts in the HRB sector from the perspective of urban energy systems to get a holistic view of their approaches. The HRB sector plays a significant role in reducing carbon emission and improving the resilience of urban energy systems. Different approaches to an HRB low-carbon transition are investigated and a brief overview of potential solutions is offered from the perspectives of improving energy efficiency, self-sufficiency and system resilience. The trends of decarbonization, decentralization and digitalization in the HRB sector allow a better alignment with transitioning urban energy systems and create cross-sectoral integration opportunities for low-carbon transition. It is also found that policy tools are powerful driving forces in China for incentivizing transition behaviors among utilities, end users and developers. Based on a comprehensive policy review, the policy implications are given. The research is geared for the situation in China but could also be used as an example for other developing countries that have similar urbanization patterns. Future research should focus on quantitative analysis, life-cycle analysis and transdisciplinary planning approaches.

Agriculture is one of the most important sectors for global anthropogenic methane (CH4) and nitrous oxide (N2O) emissions. While much attention has been paid to production-side agricultural non-CO2 greenhouse gas (ANGHG) emissions, less is known about the emissions from the consumption-based perspective. This paper aims to explore the characteristics of agricultural CH4 and N2O emissions of global major economies by using the latest emission data from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) and the recently available global multi-regional input-output model from the World Input-Output Database (WIOD). The results show that in 2014, the 42 major economies together accounted for 60.7% and 65.0% of global total direct and embodied ANGHG emissions, respectively. The consumption-based ANGHG emissions in the US, Japan, and the EU were much higher than their production-based emissions, while the converse was true for Brazil, Australia, and India. The global-average embodied ANGHG emissions per capita was 0.7 t CO2-eq, but major developing countries such as China, India, Indonesia and Mexico were all below this average value. We find that the total transfer of embodied ANGHG emissions via international trade was 622.4 Mt CO2-eq, 11.9% of the global total. China was the largest exporter of embodied ANGHG emissions, while the US was the largest importer. Most developed economies were net importers of embodied emissions. Mexico-US, China-US, China-EU, China-Japan, China-Russia, Brazil-EU, India-EU and India-US formed the main bilateral trading pairs of embodied emission flows. Examining consumption-based inventories can be useful for understanding the impacts of final demand and international trade on agricultural GHG emissions and identifying appropriate mitigation potentials along global supply chains.

Emission inventories are critical to understanding the sources of air pollutants, but have high uncertainties in China due in part to insufficient on-site measurements. In this study, we developed a method of examining, screening and applying online data from the country's improving continuous emission monitoring systems (CEMS) to reevaluate a “bottom-up” emission inventory of China's coal-fired power sector. The benefits of China's current national emission standards and ultra-low emission policy for the sector were quantified assuming their full implementation. The derived national average emission factors of SO₂, NO_x and particulate matter (PM) were 1.00, 1.00 and 0.25 kg/t-coal respectively for 2015 based on CEMS data, smaller than those of previous studies that may not fully recognize improved emission controls in recent years. The annual emissions of SO₂, NO_x and PM from the sector were recalculated at 1321, 1430 and 334 Gg respectively, 75%, 63% and 76% smaller than our estimates based on a previous approach without the benefit of CEMS data. The results imply that online measurement with proper data screening can better track the recent progress of emission controls. The emission intensity (the ratio of emissions to economic output) of Northwest China was larger than that of other regions, attributed mainly to its less intensive economy and industry. Transmission of electricity to more-developed eastern provinces raised the energy consumption and emissions of less-developed regions. Judged by 95 percentiles of flue-gas concentrations measured by CEMS, most power plants met the current national emission standards in 2015 except for those in Northwest and Northeast China, while plants that met the ultra-low emission policy were much scarcer. National SO₂, NO_x and PM emissions would further decline by 68%, 55% and 81% respectively if the ultra-low emission policy can be strictly implemented, implying the great potential of the policy for emission abatement.

China has large but uncertain coal mine methane (CMM) emissions. Inverse modeling (top-down) analyses of atmospheric methane observations can help improve the emission estimates but require reliable emission patterns as prior information. To serve this urgent need, we developed a high-resolution (0.25° × 0.25°) methane emission inventory for China’s coal mining using a recent publicly available database of more than 10000 coal mines in China for 2011. This number of coal mines is 25 and 2.5 times, respectively, more than the number available in the EDGAR v4.2 and EDGAR v4.3.2 gridded global inventories, which have been extensively used in past inverse analyses. Our inventory shows large differences with the EDGAR v4.2 as well as its more recent version, EDGAR v4.3.2. Our results suggest that China’s CMM emissions have been decreasing since 2012 on the basis of coal mining activities and assuming time-invariant emission factors but that regional trends differ greatly. Use of our inventory as prior information in future inverse modeling analyses can help better quantify CMM emissions as well as more confidently guide the future mitigation of coal to gas in China.

In this paper, we look at differences in travel behavior and location characteristics across income in Chengdu, China at two points of time, 2005 and 2016, using household travel surveys. Specifically, we compare changes over time for different income groups for Chengdu in 2005 and 2016. We find that walking or biking remains the most common mode for all income groups but higher-income households appear to have more choices depending on the proximity of their neighborhood to downtown. We also find that both average local and average regional access have worsened since 2005. Furthermore, it appears that there is less economic diversity within neighborhoods in 2016 when compared to 2005, with more locations appearing to have 40% or more of low-, middle-, or high-income households than in the past. Finally, we find that low-income households and older trip makers are more likely to walk or bike and that high-income households are the most likely to own cars and use motorized modes. Built environment characteristics like mixed land use appear to significantly reduce travel time in 2016 but do not result in higher non-motorized transport mode share. We contribute to existing literature by evaluating changes in the relationship of built environment and travel behavior during a period of rapid urbanization and economic growth in a Chinese city.

A carbon tax is potentially a policy that can reduce CO₂ emissions and mitigate climate risks, at lowest economy-wide costs. We develop a dynamic CGE model for Spain to assess the economic and environmental effects of a carbon tax, and test the double dividend (DD) hypothesis. We simulate the impact of three carbon taxes: €10, €20 and €30 per ton of CO₂. For each tax, four ‘revenue recycling’ scenarios are examined: a reduction of taxes on capital, on labor, on value-added tax, and a scenario in which revenues are not recycled. We find a DD for taxes of €10/ton and lower, within five to seven years of implementation. We estimate an annual CO₂ emissions reduction of around 10% with this tax. Under some circumstances, the DD can be achieved for a tax of €20/ton. In any case, recycling revenues to cut pre-existing taxes reduces costs of imposing carbon taxes.

China pledges to peak CO₂ emissions by 2030 or sooner under the Paris Agreement to limit global warming to 2 °C or less by the end of the century. By examining CO₂ emissions from 50 Chinese cities over the period 2000–2016, we found a close relationship between per capita emissions and per capita gross domestic product (GDP) for individual cities, following the environmental Kuznets curve, despite diverse trajectories for CO₂ emissions across the cities. Results show that carbon emissions peak for most cities at a per capita GDP (in 2011 purchasing power parity) of around US$21,000 (80% confidence interval: US$19,000 to 22,000). Applying a Monte Carlo approach to simulate the peak of per capita emissions using a Kuznets function based on China’s historical emissions, we project that emissions for China should peak at 13–16 GtCO₂ yr⁻¹ between 2021 and 2025, approximately 5–10 yr ahead of the current Paris target of 2030. We show that the challenges faced by individual types of Chinese cities in realizing low-carbon development differ significantly depending on economic structure, urban form and geographical location.

China is introducing a national carbon emission trading system (ETS), with details yet to be finalized. The ETS is expected to cover only the major emitters but it is often argued that a more comprehensive system will achieve the emission goals at lower cost. We first examine an ETS that covers both electricity and cement sectors and consider an ambitious cap starting in 2017 that will meet the official objective to reduce the carbon-GDP intensity by 60-65% by 2030 compared to 2005 levels. The two ETS-covered industries are compensated with an output-based subsidy to represent the intention to give free permits to the covered enterprises. We then consider a hybrid system where the non-ETS sectors pay a carbon tax and share in the CO₂ reduction burden. Our simulations indicate that hybrid systems will achieve the same CO₂ goals with lower permit prices and GDP losses. We also show how auctioning of the permits improves the efficiency of the ETS and the hybrid systems. Finally, we find that these CO₂ control policies are progressive in that higher incomes households bear a bigger burden.

Based on econometric estimation using data from the Chinese Urban Household Survey, we develop a preferred forecast range of 85–143 percent growth in residential per capita electricity demand over 2009–2025. Our analysis suggests that per capita income growth drives a 43% increase, with the remainder due to an unexplained time trend. Roughly one-third of the income-driven demand comes from increases in the stock of specific major appliances, particularly AC units. The other two-thirds comes from non-specific sources of income-driven growth and is based on an estimated income elasticity that falls from 0.28 to 0.11 as income rises. While the stock of refrigerators is not projected to increase, we find that they contribute nearly 20 percent of household electricity demand. Alternative plausible time trend assumptions are responsible for the wide range of 85–143 percent. Meanwhile we estimate a price elasticity of demand of −0.7. These estimates point to carbon pricing and appliance efficiency policies that could substantially reduce demand.