Harvard-China Project Papers Funded by the Harvard Global Institute

2020
Meng Gao, Jinhui Gao, Bin Zhu, Rajesh Kumar, Xiao Lu, Shaojie Song, Yuzhong Zhang, Beixi Jia, Peng Wang, Gufran Beig, Jianlin Hu, Qi Ying, Hongliang Zhang, Peter Sherman, and Michael B. McElroy. 2020. “Ozone pollution over China and India: seasonality and sources.” Atmospheric Chemistry and Physics, 20, 7, Pp. 4399-4414. Publisher's VersionAbstract
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.
ACP_Full_Text
Jialin Liu, Fangyan Cheng, J. William Munger, Timothy G. Whitby, Peng Jiang, Siyue Chen, Weiwen Ji, and Xiuling Man. 2020. “Precipitation extremes influence patterns and partitioning of evapotranspiration and transpiration in a deciduous boreal larch forest.” Agricultural and Forest Meteorology, 287, 15 June, Pp. 107936. Publisher's VersionAbstract
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.
Jing Cao, Mun S Ho, Wenhao Hu, and Dale W Jorgenson. 2020. “Urban household consumption in China: price, income and demographic effects.” Review of Development Economics, 152, 25 October, Pp. 810-833. Publisher's Version
2019
Mengyao Han, Bo Zhang, Yuqing Zhang, and Chenghe Guan. 2019. “Agricultural CH4 and N2O emissions of major economies: Consumption- vs. production-based perspectives.” Journal of Cleaner Production, 210, 10 February, Pp. 276-286. Publisher's VersionAbstract

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.

Yan Zhang, Xin Bo, Yu Zhao, and Chris P. Nielsen. 2019. “Benefits of current and future policies on emissions of China's coal-fired power sector indicated by continuous emission monitoring.” Environmental Pollution, 251, August, Pp. 415-424. Publisher's VersionAbstract
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, NOx 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, NOx 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, NOx 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.
Jianxiong Sheng, Shaojie Song, Yuzhong Zhang, Ronald G. Prinn, and Greet Janssens-Maenhout. 2019. “Bottom-up estimates of coal mine methane emissions in China: A gridded inventory, emission factors, and trends.” Environmental Science and Technology Letters, 6, 8, Pp. 473-478. Publisher's VersionAbstract
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.
ESTLett paper.pdf
Sumeeta Srinivasan, Chenghe Guan, and Chris P. Nielsen. 2019. “Built environment, income and travel behavior: Change in the city of Chengdu 2005-2016.” International Journal of Sustainable Transportation, 14, 10, Pp. 749-760. Publisher's VersionAbstract
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.
Jaume Freire-González and Mun S. Ho. 2019. “Carbon taxes and the double dividend hypothesis in a recursive-dynamic CGE model for Spain.” Economic Systems Research, 31, 2, Pp. 267-284. Publisher's VersionAbstract
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’s CO2 peak before 2030 implied from diverse characteristics and growth of cities
Haikun Wang, Xi Lu, Yu Deng, Yaoguang Sun, Chris P. Nielsen, Yifan Liu, Ge Zhu, Maoliang Bu, Jun Bi, and Michael B. McElroy. 2019. “China’s CO2 peak before 2030 implied from diverse characteristics and growth of cities.” Nature Sustainability, 2, Pp. 748–754. Publisher's VersionAbstract

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.

Jing Cao, Mun S. Ho, Dale W. Jorgenson, and Chris P. Nielsen. 2019. “China’s emissions trading system and an ETS-carbon tax hybrid.” Energy Economics, 81, June, Pp. 741-753. Publisher's VersionAbstract
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.
Appendix
Jing Cao, Mun S. Ho, Yating Li, Richard G. Newell, and William A. Pizer. 2019. “Chinese residential electricity consumption estimation and forecast using micro-data.” Resource and Energy Economics, 56, May, Pp. 6-27. Publisher's VersionAbstract
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.
Chenghe Guan, Michael Keith, and Andy Hong. 2019. “Designing walkable cities and neighborhoods in the era of urban big data.” Urban Planning International, 34, 5, Pp. 9-15. Publisher's VersionAbstract
In this paper, we discuss walkable cities from the perspective of urban planning and design in the era of digitalization and urban big data. We start with a brief review on historical walkable cities schemes; followed by a deliberation on what a walkable city is and what the spatial elements of a walkable city are; and a discussion on the emerging themes and empirical methods to measure the spatial and urban design features of a walkable city. The first part of this paper looks at key urban design propositions and how they were proposed to promote walkability. The second part of this paper discusses the concept of walkability, which is fundamental to designing a walkable city. We emphasize both the physical (walkways, adjacent uses, space) and the perceived aspects (safety, comfort, enjoyment), and then we look at the variety of spatial elements constituting a walkable city. The third part of this paper looks at the emerging themes for designing walkable cities and neighborhoods. We discuss the application of urban big data enabled by growing computational powers and related empirical methods and interdisciplinary approaches including spatial planning, urban design, urban ecology, and public health. This paper aims to provide a holistic approach toward understanding of urban design and walkability, re-evaluate the spatial elements to build walkable cities, and discuss future policy interventions.
Chenghe Guan, Sumeeta Srinivasan, and Chris P. Nielsen. 2019. “Does neighborhood form influence low-carbon transportation in China?” Transportation Research Part D: Transport and Environment, 67, February, Pp. 406-420. Publisher's VersionAbstract
Developing less auto-dependent urban forms and promoting low-carbon transportation (LCT) are challenges facing our cities. Previous literature has supported the association between neighborhood form and low-carbon travel behaviour. Several studies have attempted to measure neighborhood forms focusing on physical built-environment factors such as population and employment density and socio-economic conditions such as income and race. We find that these characteristics may not be sufficiently fine-grained to differentiate between neighborhoods in Chinese cities. This research assesses characteristics of neighborhood spatial configuration that may influence the choice of LCT modes in the context of dense Chinese cities. Urban-form data from 40 neighborhoods in Chengdu, China, along with a travel behaviour survey of households conducted in 2016, were used to generate several measures of land use diversity and accessibility for each neighborhood. We use principle component analysis (PCA) to group these variables into dimensions that could be used to classify the neighborhoods. We then estimate regression models of low-carbon mode choices such as walking, bicycling, and transit to better understand the significance of these built-environment differences at the neighbourhood level. We find that, first, members of households do choose to walk or bike or take transit to work provided there is relatively high population density and sufficient access to public transit and jobs. Second, land-use diversity alone was not found to be significant in affecting LCT mode choice. Third, the proliferation of gated communities was found to reduce overall spatial connectivity within neighborhoods and had a negative effect on choice of LCT.
Jing Cao, Mun S Ho, and Wenhao Hu. 2019. “Energy consumption of urban households in China.” China Economic Review, 58, 101343. Publisher's VersionAbstract
We estimate China urban household energy demand as part of a complete system of consumption demand so that it can be used in economy-wide models. This allows us to derive cross-price elasticities unlike studies which focus on one type of energy. We implement a two-stage approach and explicitly account for electricity, domestic fuels and transportation demand in the first stage and gasoline, coal, LPG and gas demand in the second stage. We find income inelastic demand for electricity and home energy, but the elasticity is higher than estimates in the rich countries. Demand for total transportation is income elastic. The price elasticity for electricity is estimated to be −0.5 and in the range of other estimates for China, and similar to long-run elasticities estimated for the U.S.
Xi Lu, Liang Cao, Haikun Wang, Wei Peng, Jia Xing, Shuxiao Wang, Siyi Cai, Bo Shen, Qing Yang, Chris P. Nielsen, and Michael B. McElroy. 2019. “Gasification of coal and biomass as a net carbon-negative power source for environment-friendly electricity generation in China.” Proceedings of the National Academy of Sciences, 116, 17, Pp. 8206-8213. Publisher's VersionAbstract
Realizing the goal of the Paris Agreement to limit global warming to 2 °C by the end of this century will most likely require deployment of carbon-negative technologies. It is particularly important that China, as the world’s top carbon emitter, avoids being locked into carbon-intensive, coal-fired power-generation technologies and undertakes a smooth transition from high- to negative-carbon electricity production. We focus here on deploying a combination of coal and biomass energy to produce electricity in China using an integrated gasification cycle system combined with carbon capture and storage (CBECCS). Such a system will also reduce air pollutant emissions, thus contributing to China’s near-term goal of improving air quality. We evaluate the bus-bar electricity-generation prices for CBECCS with mixing ratios of crop residues varying from 0 to 100%, as well as associated costs for carbon mitigation and cobenefits for air quality. We find that CBECCS systems employing a crop residue ratio of 35% could produce electricity with net-zero life-cycle emissions of greenhouse gases, with a levelized cost of electricity of no more than 9.2 US cents per kilowatt hour. A carbon price of approximately $52.0 per ton would make CBECCS cost-competitive with pulverized coal power plants. Therefore, our results provide critical insights for designing a CBECCS strategy in China to harness near-term air-quality cobenefits while laying the foundation for achieving negative carbon emissions in the long run.
PNAS paper.pdf
Peter Sherman, Meng Gao, Shaojie Song, Patrick Ohiomoba, Alex Archibald, and Michael B. McElroy. 2019. “The influence of dynamics and emissions changes on China’s wintertime haze.” Journal of Applied Meteorology and Climatology, 58, 7, Pp. 1603-1611. Publisher's VersionAbstract

Haze days induced by aerosol pollution in North and East China have posed a persistent and growing problem over the past few decades. These events are particularly threatening to densely-populated cities such as Beijing. While the sources of this pollution are predominantly anthropogenic, natural climate variations may also play a role in allowing for atmospheric conditions conducive to formation of severe haze episodes over populated areas. Here, an investigation is conducted into the effects of changes in global dynamics and emissions on air quality in China’s polluted regions using 35 simulations developed from the Community Earth Systems Model Large Ensemble (CESM LENS) run over the period 1920-2100. It is shown that internal variability significantly modulates aerosol optical depth (AOD) over China; it takes roughly a decade for the forced response to balance the effects from internal variability even in China’s most polluted regions. Random forest regressions are used to accurately model (R2 > 0.9) wintertime AOD using just climate oscillations, the month of the year and emissions. How different phases of each oscillation affect aerosol loading are projected using these regressions. AOD responses are identified for each oscillation, with particularly strong responses from El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO). As ENSO can be projected a few months in advance and improvements in linear inverse modelling (LIM) may yield a similar predictability for the PDO, results of this study offer opportunities to improve the predictability of China’s severe wintertime haze events, and to inform policy options that could mitigate subsequent health impacts.

JAMC paper
Xingning Han, Xinyu Chen, Michael B. McElroy, Shiwu Liao, Chris P. Nielsen, and Jinyu Wen. 2019. “Modeling formulation and validation for accelerated simulation and flexibility assessment on large scale power systems under higher renewable penetrations.” Applied Energy, 237, 1 March, Pp. 145-154. Publisher's VersionAbstract
Deploying high penetration of variable renewables represents a critical pathway for decarbonizing the power sector. Hydro power (including pumped-hydro), batteries, and fast responding thermal units are essential in providing system flexibility at elevated renewable penetration. How to quantify the merit of flexibility from these sources in accommodating variable renewables, and to evaluate the operational costs considering system flexibility constraints have been central challenges for future power system planning. This paper presents an improved linear formulation of the unit commitment model adopting unit grouping techniques to expedite evaluation of the curtailment of renewables and operational costs for large-scale power systems. All decision variables in this formulation are continuous, and all chronological constraints are formulated subsequently. Tested based on actual data from a regional power system in China, the computational speed of the model is more than 20,000 times faster than the rigorous unit commitment model, with less than 1% difference in results. Hourly simulation for an entire year takes less than 3 min. The results demonstrate strong potential to apply the proposed model to long term planning related issues, such as flexibility assessment, wind curtailment analysis, and operational cost evaluation, which could set a methodological foundation for evaluating the optimal combination of wind, solar and hydro investments.
S.J. Song, M. Gao, W.Q. Xu, Y.L. Sun, D.R. Worsnop, J.T. Jayne, Y.Z. Zhang, L. Zhu, M. Li, Z. Zhou, C.L. Cheng, Y.B. Lv, Y. Wang, W. Peng, X.B. Xu, N. Lin, Y.X. Wang, S.X. Wang, J. W. Munger, D. Jacob, and M.B. McElroy. 2019. “Possible heterogeneous hydroxymethanesulfonate (HMS) chemistry in northern China winter haze and implications for rapid sulfate formation.” Atmospheric Chemistry and Physics, 19, Pp. 1357-1371. Publisher's VersionAbstract
The chemical mechanisms responsible for rapid sulfate production, an important driver of winter haze formation in northern China, remain unclear. Here, we propose a potentially important heterogeneous hydroxymethanesulfonate (HMS) chemical mechanism. Through analyzing field measurements with aerosol mass spectrometry, we show evidence for a possible significant existence in haze aerosols of organosulfur primarily as HMS, misidentified as sulfate in previous observations. We estimate that HMS can account for up to about one-third of the sulfate concentrations unexplained by current air quality models. Heterogeneous production of HMS by SO2 and formaldehyde is favored under northern China winter haze conditions due to high aerosol water content, moderately acidic pH values, high gaseous precursor levels, and low temperature. These analyses identify an unappreciated importance of formaldehyde in secondary aerosol formation and call for more research on sources and on the chemistry of formaldehyde in northern China winter.
ACP paper
The potential of photovoltaics to power the Belt and Road Initiative
Shi Chen, Xi Lu, Yufei Miao, Yu Deng, Chris P. Nielsen, Noah Elbot, Yuanchen Wang, Kathryn G. Logan, Michael B. McElroy, and Jiming Hao. 2019. “The potential of photovoltaics to power the Belt and Road Initiative.” Joule, 3, 8, Pp. 1895-1912. Publisher's VersionAbstract
Construction of carbon-intensive energy infrastructure is well underway under the Belt & Road Initiative (BRI), challenging the global climate target. Regionally abundant solar power could provide an alternative for electricity generation. An integrative spatial model was developed to evaluate the technical potential of solar photovoltaic power. The influence of impacting factors was quantified systematically on an hourly basis. Results suggest that the electricity potential for the BRI region reaches 448.9 PWh annually, 41.3 times the regional demand for electricity in 2016. Tapping 3.7% of the potential through deploying 7.8 TW capacity could satisfy the regional electricity demand projected for 2030, requiring an investment of approximately 11.2 trillion 2017 USD and a commitment in land area of 88,426 km2, approximately 0.9% of China’s total. Countries endowed with 70.7% of the overall potential consume only 30.1% of regional electricity. The imbalance underscores the advantage of regional cooperation and investments in interconnected grids.
Graphic Summary Joule full paper.pdf
Meng Gao, Peter Sherman, Shaojie Song, Yueyue Yu, Zhiwei Wu, and Michael B. McElroy. 2019. “Seasonal prediction of Indian wintertime aerosol pollution using the Ocean Memory Effect.” Science Advances, 5, 7. Publisher's VersionAbstract
As China makes every effort to control air pollution, India emerges as the world’s most polluted country, receiving worldwide attention with frequent winter (boreal) haze extremes. In this study, we found that the interannual variability of wintertime aerosol pollution over northern India is regulated mainly by a combination of El Niño and the Antarctic Oscillation (AAO). Both El Niño sea surface temperature (SST) anomalies and AAO-induced Indian Ocean Meridional Dipole SST anomalies can persist from autumn to winter, offering prospects for a prewinter forecast of wintertime aerosol pollution over northern India. We constructed a multivariable regression model incorporating El Niño and AAO indices for autumn to predict wintertime AOD. The prediction exhibits a high degree of consistency with observation, with a correlation coefficient of 0.78 (P < 0.01). This statistical model could allow the Indian government to forecast aerosol pollution conditions in winter and accordingly improve plans for pollution control.
Science_Advances_Paper.pdf

Pages