Minghao Zhuang, Xi Lu, Wei Peng, Yanfen Wang, Jianxiao Wang, Chris P. Nielsen, and Michael B. McElroy. 2021. “Opportunities for household energy on the Qinghai-Tibet Plateau in line with United Nations’ Sustainable Development Goals.” Renewable and Sustainable Energy Reviews, 144, July, Pp. 110982.Abstract
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.
Peter Sherman, Shaojie Song, Xinyu Chen, and Michael B. McElroy. 2021. “Projected changes in wind power potential over China and India in high resolution climate models.” Environmental Research Letters, 16, 3, Pp. 034057. Publisher's VersionAbstract
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.
Qing Yang, Hewen Zhou, Pietro Bartocci, Francesco Fantozzi, Ondřej Mašek, Foster Agblevor, Zhiyu Wei, Haiping Yang, Hanping Chen, Xi Lu, Guoqian Chen, Chuguang Zheng, Chris Nielsen, and Michael McElroy. 2021. “Prospective contributions of biomass pyrolysis to China’s 2050 carbon reduction and renewable energy goals.” Nature Communications, 12, 168. Publisher's VersionAbstract
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. 
Tianguang Lu, Xinyu Chen, Michael B. McElroy, Chris Nielsen, Wu Qiuwei, Hongying He, and Qian Ai. 2021. “A reinforcement learning-based decision system for electricity pricing plan selection by smart grid end users.” IEEE Transactions on Smart Grid, 12, 3, Pp. 2176-2187. Publisher's VersionAbstract
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.
Chenghe Guan, Jihoon Song, Michael Keith, Bo Zhang, Yuki Akiyama, Liangjun Da, Ryosuke Shibasaki, and Taisei Sato. 2021. “Seasonal variations of park visitor volume and park service area in Tokyo: A mixed-method approach combining big data and field observations.” Urban Forestry & Urban Greening, 58, March, Pp. 126973. Publisher's VersionAbstract
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.
Peter Sherman, Meng Gao, Shaojie Song, Alex T. Archibald, Nathan Luke Abraham, Jean-François Lamarque, Drew Shindell, Gregory Faluvegi, and Michael B. McElroy. 2021. “Sensitivity of modeled Indian Monsoon to Chinese and Indian aerosol emissions.” Atmospheric Chemistry and Physics, 21, 5, Pp. 3593–3605. Publisher's VersionAbstract
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.
Jaume Freire-González and Mun S. Ho. 2021. “Voluntary actions in households and climate change mitigation.” Journal of Cleaner Production, 321, 25 October, Pp. 128930. Publisher's VersionAbstract
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.
Jing Cao, Mun S. Ho, and Wenhao Hu. 2020. “Analyzing carbon price policies using a general equilibrium model with household energy demand functions.” In Measuring Economic Growth and Productivity: Foundations, KLEMS Production Models, and Extensions, edited by Barbara Fraumeni. Cambridge, MA: Academic Press. Publisher's VersionAbstract

Multi-sector general equilibrium models are used to simulate the effects of environmental policies on industry output and consumption at disaggregated levels. The specification of household demand in such models often use simpler forms such as CES or Linear Expenditure Systems since there are few estimates of more flexible systems. We estimate a 2-stage translog utility function that explicitly accounts for detailed energy expenditures to allow us to capture the price and income effects more accurately than these simpler forms. We incorporate this into a China growth model to simulate the effects of a carbon price to achieve the government targets for the Climate Change (Paris) agreements.

Final Manuscript in DASH.
An edited volume dedicated to Prof. Dale W. Jorgenson by his students and collaborators.

Jing Cao, Mun S Ho, and Rong Ma. 2020. “Analyzing carbon pricing policies using a general equilibrium model with production parameters estimated using firm data.” Energy Economics, 92, October, Pp. 104958. Publisher's VersionAbstract

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.

Meng Gao, Zirui Liu, Bo Zheng, Dongsheng Ji, Peter Sherman, Shaojie Song, Jinyuan Xin, Cheng Liu, Yuesi Wang, Qiang Zhang, Jia Xing, Jingkun Jiang, Zifa Wang, Gregory R. Carmichael, and Michael B. McElroy. 2020. “China's emission control strategies have suppressed unfavorable influences of climate on wintertime PM2.5 concentrations in Beijing since 2002.” Atmospheric Chemistry and Physics, 20, 3, Pp. 1497-1505. Publisher's VersionAbstract
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.
Haotian Zheng, Shaojie Song, Golam Sarwar, Masao Gen, Shuxiao Wang, Dian Ding, Xing Chang, Shuping Zhang, Jia Xing, Yele Sun, Dongsheng Ji, Chak Chan, Jian Gao, and Michael B. McElroy. 2020. “Contribution of particulate nitrate photolysis to heterogeneous sulfate formation for winter haze in China.” Environmental Science & Technology Letters , 7, 9, Pp. 632–638. Publisher's VersionAbstract
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.
Chenghe Guan, Jihoon Song, Michael Keith, Yuki Akiyama, Ryosuke Shibasaki, and Taisei Sato. 2020. “Delineating urban park catchment areas using mobile phone data: A case study of Tokyo.” Computers, Environment and Urban Systems, 81, May, Pp. 101474. Publisher's VersionAbstract
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.
Fei Xiao, Tianguang Lu, Qian Ai, Xiaolong Wang, Xinyu Chen, Sidun Fang, and Qiuwei Wu. 2020. “Design and implementation of a data-driven approach to visualizing power quality.” IEEE Transactions on Smart Grid, 114, 5, Pp. 4366-4379. Publisher's VersionAbstract
Numerous underlying causes of power-quality (PQ) disturbances have enhanced the application of situational awareness to power systems. This application provides an optimal overall response for contingencies. With measurement data acquired by a multi-source PQ monitoring system, we propose an interactive visualization tool for PQ disturbance data based on a geographic information system (GIS). This tool demonstrates the spatio–temporal distribution of the PQ disturbance events and the cross-correlation between PQ records and environmental factors, leveraging Getis statistics and random matrix theory. A methodology based on entity matching is also introduced to analyze the underlying causes of PQ disturbance events. Based on real-world data obtained from an actual power system, offline and online PQ data visualization scenarios are provided to verify the effectiveness and robustness of the proposed framework.
X. Lu, L. Zhang, T. Wu, M. S. Long, J. Wang, D.J. Jacob, F. Zhang, J. Zhang, S. D. Eastham, L. Hu, L. Zhu, X. Liu, and M. Wei. 2020. “Development of the global atmospheric general circulation-chemistry model BCC-GEOS-Chem v1.0: model description and evaluation.” Geoscientific Model Development, 13, 9, Pp. 3817–3838. Publisher's VersionAbstract
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 troposphericHOx–NOx–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.
Jing Cao, Mun S. Ho, Wenhao Hu, and Dale Jorgenson. 2020. “Effective labor supply and growth outlook in China.” China Economic Review, 61, June, Pp. 101398. Publisher's VersionAbstract
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.
Richard Goettle, Mun S. Ho, and Peter Wilcoxen. 2020. “Emissions accounting and carbon tax incidence in CGE models: bottom-up versus top-down.” In Measuring Economic Growth and Productivity: Foundations, KLEMS Production Models, and Extensions, edited by Fraumeni, B, 1st ed. Cambridge, MA: Academic Press. Publisher's VersionAbstract
Multi-sector general equilibrium models are the work-horses used to analyze the impact of carbon prices in climate policy discussions. Such models often have distinct industries to represent coal, liquid fuels, and gas production where the output over time is represented by quantity and price indexes. The industries that buy these fuels, however, do not use a common homogenous quantity (e.g., steam coal vs. metallurgical coal) and have distinct purchasing price indexes. In accounting for energy use or CO2 emissions, modelers choose to attach coefficients either bottom-up to a sector specific input index or top-down to an average output index and this choice has a direct bearing on the incidence of carbon taxation. We discuss how different accounting methods for the differences in prices can have a large effect on the simulated impact of carbon prices. We emphasize the importance for modelers to be explicit about their methods.
An edited volume dedicated to Prof. Dale W. Jorgenson by his students and collaborators.  Final Manuscript in DASH
Jing Cao, Mun S. Ho, Wenhao Hu, and Dale W. Jorgensen. 2020. “Estimating flexible consumption functions for urban and rural households in China.” China Economic Review, 61, June, Pp. 101453. Publisher's VersionAbstract

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.

Archana Dayalu, J. William Munger, Yuxuan Wang, Steven C. Wofsy, Yu Zhao, Thomas Nehrkorn, Chris P. Nielsen, Michael B. McElroy, and Rachel Chang. 2020. “Evaluating China's anthropogenic CO2 emissions inventories: a northern China case study using continuous surface observations from 2005 to 2009.” Atmospheric Chemistry and Physics, 20, 6, Pp. 3569–3588. Publisher's VersionAbstract
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.
Haikun Wang, Xiaojing He, Xinyu Liang, Ernani F. Choma, Yifan Liu, Li Shan, Haotian Zheng, Shaojun Zhang, Chris Nielsen, Shuxiao Wang, Ye Wu, and John Evans. 2020. “Health benefits of on-road transportation pollution control programs in China.” Proceedings of the National Academy of Sciences, 117, 41, Pp. 25370-25377. Publisher's VersionAbstract
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 (PM2.5) 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 PM2.5 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 PM2.5 and O3 in different regions, to sustain improvement in air quality and public health given continuing swift growth in China’s vehicle population.
Peter Sherman, Eli Tziperman, Clara Deser, and Michael B. McElroy. 2020. “Historical and future roles of internal atmospheric variability in modulating summertime Greenland Ice Sheet melt.” Geophysical Research Letters, 47, 6, Pp. e2019GL086913. Publisher's VersionAbstract
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.