Harvard-China Project Papers Funded by the Harvard Global Institute

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.

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 on 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.

This paper provides retrospective firm-level evidence on the effectiveness of China’s carbon market pilots in reducing emissions in the electricity sector. We show that the carbon emission trading system (ETS) has no effect on changing coal efficiency of regulated coal- fired power plants. Although we find a significant reduction in coal consumption associated with ETS participation, this reduction was achieved by reducing electricity production. The output contraction in the treated plants is not due to their optimizing behavior but is likely driven by government decisions, because the impacts of emission permits on marginal costs are small relative to the controlled electricity prices and the reduction is associated with financial losses. In addition, we find no evidence of carbon leakage to other provinces, but a significant increase in the production of non-coal-fired power plants in the ETS regions. 

Policy simulation results of Computable General Equilibrium (CGE) models largely hinge on the choices of substitution elasticities among key input factors. Currently, most CGE models rely on the common elasticities estimated from aggregated data, such as the GTAP model elasticity parameters. Using firm level data, we apply the control function method to estimate CES production functions with capital, labor and energy inputs and find significant heterogeneity in substitution elasticities across different industries. Our capital-labor substitution elasticities are much lower than the GTAP values while our energy elasticities are higher. We then incorporate these estimated elasticities into a CGE model to simulate China's carbon pricing policies and compare with the results using GTAP parameters. Our less elastic K-L substitution lead to lower base case GDP growth, but our more elastic energy substitution lead to lower coal use and carbon emissions. In the carbon tax policy exercises, we find that our elasticities lead to easier reductions in coal use and carbon emissions.

Severe wintertime PM_2.5 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 PM_2.5 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 PM_2.5 in Beijing, with 7 fewer haze days per winter on average. Given the overestimation of PM_2.5 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.

Nitrate and sulfate are two key components of airborne particulate matter (PM). While multiple formation mechanisms have been proposed for sulfate, current air quality models commonly underestimate its concentrations and mass fractions during northern China winter haze events. On the other hand, current models usually overestimate the mass fractions of nitrate. Very recently, laboratory studies have proposed that nitrous acid (N(III)) produced by particulate nitrate photolysis can oxidize sulfur dioxide to produce sulfate. Here, for the first time, we parameterize this heterogeneous mechanism into the state-of-the-art Community Multi-scale Air Quality (CMAQ) model and quantify its contributions to sulfate formation. We find that the significance of this mechanism mainly depends on the enhancement effects (by 1–3 orders of magnitude as suggested by the available experimental studies) of nitrate photolysis rate constants in aerosol liquid water compared to that in the gas phase. Comparisons between model simulations and in-situ observations in Beijing suggest that this pathway can explain about 15% (assuming an enhancement factor (EF) of 10) to 65% (assuming EF = 100) of the model–observation gaps in sulfate concentrations during winter haze. Our study strongly calls for future research on reducing the uncertainty in EF.

Urban parks can offer both physical and psychological health benefits to urban dwellers and provide social, economic, and environmental benefits to society. Earlier research on the usage of urban parks relied on fixed distance or walking time to delineate urban park catchment areas. However, actual catchment areas can be affected by many factors other than park surface areas, such as social capital cultivation, cultural adaptation, climate and seasonal variation, and park function and facilities provided. This study advanced this method by using mobile phone data to delineate urban park catchment area. The study area is the 23 special wards of Tokyo or tokubetsu-ku, the core of the capital of Japan. The location data of over 1 million anonymous mobile phone users was collected in 2011. The results show that: (1) the park catchment areas vary significantly by park surface areas: people use smaller parks nearby but also travel further to larger parks; (2) even for the parks in the same size category, there are notable differences in the spatial pattern of visitors, which cannot be simply summarized with average distance or catchment radius; and (3) almost all the parks, regardless of its size and function, had the highest user density right around the vicinity, exemplified by the density-distance function closely follow a decay trend line within 1-2 km radius of the park. As such, this study used the density threshold and density-distance function to measure park catchment. We concluded that the application of mobile phone location data can improve our understanding of an urban park catchment area, provide useful information and methods to analyze the usage of urban parks, and can aid in the planning and policy-making of urban parks.

Chemistry plays an indispensable role in investigations of the atmosphere; however, many climate models either ignore or greatly simplify atmospheric chemistry, limiting both their accuracy and their scope. We present the development and evaluation of the online global atmospheric chemical model BCC-GEOS-Chem v1.0, coupling the GEOS-Chem chemical transport model (CTM) as an atmospheric chemistry component in the Beijing Climate Center atmospheric general circulation model (BCC-AGCM). The GEOS-Chem atmospheric chemistry component includes detailed troposphericHO_x–NO_x–volatile organic compounds–ozone–bromine–aerosol chemistry and online dry and wet deposition schemes. We then demonstrate the new capabilities of BCC-GEOS-Chem v1.0 relative to the base BCC-AGCM model through a 3-year (2012–2014) simulation with anthropogenic emissions from the Community Emissions Data System (CEDS) used in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The model captures well the spatial distributions and seasonal variations in tropospheric ozone, with seasonal mean biases of 0.4–2.2 ppbv at 700–400 hPa compared to satellite observations and within 10 ppbv at the surface to 500 hPa compared to global ozonesonde observations. The model has larger high-ozone biases over the tropics which we attribute to an overestimate of ozone chemical production. It underestimates ozone in the upper troposphere which is likely due either to the use of a simplified stratospheric ozone scheme or to biases in estimated stratosphere–troposphere exchange dynamics. The model diagnoses the global tropospheric ozone burden, OH concentration, and methane chemical lifetime to be 336 Tg, 1.16×10⁶ molecule cm⁻³, and 8.3 years, respectively, which is consistent with recent multimodel assessments. The spatiotemporal distributions of NO₂, CO, SO₂, CH₂O, and aerosol optical depth are generally in agreement with satellite observations. The development of BCC-GEOS-Chem v1.0 represents an important step for the development of fully coupled earth system models (ESMs) in China.

The falling projections of working-age population in China has led to predictions of much slower economic growth. We consider three mechanisms that could contribute to higher effective labor supply growth – further improvement in educational attainment due to cohort replacement and rising college enrollment, improvement in aggregate labor quality due to urbanization, and higher labor force participation due to later retirement. We find that these factors result in a projected growth rate of effective labor input of 0.40% for 2015-2030 compared to -0.60% for working age population. As a result, the projected growth rate of GDP will be 5.80% for 2015-2030 compared to 5.23% if these factors are ignored.

There are few comprehensive studies of household consumption in China due to data restrictions. This prevents the calculation of inequality indices based on consumption. Secondly, this makes a comprehensive analysis of policies that affect consumption difficult; economy-wide models used for analysis often have to employ simple consumption forms with unit income elasticities. We estimate a translog demand system distinguished by demographic characteristics, giving price and income elasticities that should be useful for policy analysis. We estimate separate functions for urban and rural households using household expenditure data and detailed commodity prices (1995-2006). This allows future analysis of social welfare and inequality based on consumption to supplement existing studies based on income. To illustrate an application of the model, we project consumption composition based on projected prices, incomes and demographic changes – aging, education improvement and urbanization.

China has pledged reduction of carbon dioxide (CO₂) emissions per unit of gross domestic product (GDP) by 60 %–65 % relative to 2005 levels, and to peak carbon emissions overall by 2030. However, the lack of observational data and disagreement among the many available inventories makes it difficult for China to track progress toward these goals and evaluate the efficacy of control measures. To demonstrate the value of atmospheric observations for constraining CO₂ inventories we track the ability of CO₂ concentrations predicted from three different CO₂ inventories to match a unique multi-year continuous record of atmospheric CO₂. Our analysis time window includes the key commitment period for the Paris Agreement (2005) and the Beijing Olympics (2008). One inventory is China-specific and two are spatial subsets of global inventories. The inventories differ in spatial resolution, basis in national or subnational statistics, and reliance on global or China-specific emission factors. We use a unique set of historical atmospheric observations from 2005 to 2009 to evaluate the three CO₂ emissions inventories within China's heavily industrialized and populated northern region accounting for ∼33 %–41 % of national emissions. Each anthropogenic inventory is combined with estimates of biogenic CO₂ within a high-resolution atmospheric transport framework to model the time series of CO₂ observations. To convert the model–observation mismatch from mixing ratio to mass emission rates we distribute it over a region encompassing 90 % of the total surface influence in seasonal (annual) averaged back-trajectory footprints (L_0.90 region). The L_0.90 region roughly corresponds to northern China. Except for the peak growing season, where assessment of anthropogenic emissions is entangled with the strong vegetation signal, we find the China-specific inventory based on subnational data and domestic field studies agrees significantly better with observations than the global inventories at all timescales. Averaged over the study time period, the unscaled China-specific inventory reports substantially larger annual emissions for northern China (30 %) and China as a whole (20 %) than the two unscaled global inventories. Our results, exploiting a robust time series of continuous observations, lend support to the rates and geographic distribution in the China-specific inventory Though even long-term observations at a single site reveal differences among inventories, exploring inventory discrepancy over all of China requires a denser observational network in future efforts to measure and verify CO₂ emissions for China both regionally and nationally. We find that carbon intensity in the northern China region has decreased by 47 % from 2005 to 2009, from approximately 4 kg of CO₂ per USD (note that all references to USD in this paper refer to USD adjusted for purchasing power parity, PPP) in 2005 to about 2 kg of CO₂ per USD in 2009 (Fig. 9c). However, the corresponding 18 % increase in absolute emissions over the same time period affirms a critical point that carbon intensity targets in emerging economies can be at odds with making real climate progress. Our results provide an important quantification of model–observation mismatch, supporting the increased use and development of China-specific inventories in tracking China's progress as a whole towards reducing emissions. We emphasize that this work presents a methodology for extending the analysis to other inventories and is intended to be a comparison of a subset of anthropogenic CO₂ emissions rates from inventories that were readily available at the time this research began. For this study's analysis time period, there was not enough spatially distinct observational data to conduct an optimization of the inventories. The primary intent of the comparisons presented here is not to judge specific inventories, but to demonstrate that even a single site with a long record of high-time-resolution observations can identify major differences among inventories that manifest as biases in the model–data comparison. This study provides a baseline analysis for evaluating emissions from a small but important region within China, as well a guide for determining optimal locations for future ground-based measurement sites.

China started to implement comprehensive measures to mitigate traffic pollution at the end of 1990s, but the comprehensive effects, especially on ambient air quality and public health, have not yet been systematically evaluated. In this study, we analyze the effects of vehicle emission control measures on ambient air pollution and associated deaths attributable to long-term exposures of fine particulate matter (PM_2.5) and O₃ based on an integrated research framework that combines scenario analysis, air quality modeling, and population health risk assessment. We find that the total impact of these control measures was substantial. Vehicular emissions during 1998–2015 would have been 2–3 times as large as they actually were, had those measures not been implemented. The national population-weighted annual average concentrations of PM_2.5 and O₃ in 2015 would have been higher by 11.7 μg/m³ and 8.3 parts per billion, respectively, and the number of deaths attributable to 2015 air pollution would have been higher by 510 thousand (95% confidence interval: 360 thousand to 730 thousand) without these controls. Our analysis shows a concentration of mortality impacts in densely populated urban areas, motivating local policymakers to design stringent vehicle emission control policies. The results imply that vehicle emission control will require policy designs that are more multifaceted than traditional controls, primarily represented by the strict emission standards, with careful consideration of the challenges in coordinated mitigation of both PM_2.5 and O₃ in different regions, to sustain improvement in air quality and public health given continuing swift growth in China’s vehicle population.

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.