2019
Jianglong Li, Chang Chen, and Hongxun Liu. 2019. “
Transition from non-commercial to commercial energy in rural China: Insights from the accessibility and affordability.” Energy Policy, 127, Pp. 392-403.
Publisher's VersionAbstractRural components are integral parts of China's economy, and hundreds of millions of China's residents still live in
rural areas. Rural residents heavily depend on non-commercial energy due to the inaccessibility and unaffordability of
commercial energy. Conventional use of solid
biomass fuels threatens
public health as well as environmental and ecological sustainability. Thus,
rural energy transition must be promoted. By using a new
dataset, we show China's rural energy transition to gain insights on where, how, and why this transition occurs in rural households. Unlike previous views, we find that after considering non-commercial energy, the per
capita consumption of rural
residential energy is considerably larger than that of urban counterparts. Moreover, migrations from rural to
urban areas decrease rather than increase residential
energy consumption. Furthermore, rural energy transition from low to high quality depresses energy consumption. Our results demonstrate how accessibility and
affordability affect the fuel preferences of rural residents, thereby enabling us to identify the mechanisms of rural energy transition. We provide some insights and policy implications on the routes of China's rural energy transition, which may be further extended to other emerging and
developing countries due to their similar rural energy use.
James K. Hammitt, Fangli Geng, Xiaoqi Guo, and Chris P. Nielsen. 2019. “
Valuing mortality risk in China: Comparing stated-preference estimates from 2005 and 2016.” Journal of Risk & Uncertainty, 58, 2-3, Pp. 167–186.
Publisher's VersionAbstractWe estimate the marginal rate of substitution of income for reduction in current annual mortality risk (the “value per statistical life” or VSL) using stated-preference surveys administered to independent samples of the general population of Chengdu, China in 2005 and 2016. We evaluate the quality of estimates by the theoretical criteria that willingness to pay (WTP) for risk reduction should be strictly positive and nearly proportional to the magnitude of the risk reduction (evaluated by comparing answers between respondents) and test the effect of excluding respondents whose answers violate these criteria. For subsamples of respondents that satisfy the criteria, point estimates of the sensitivity of WTP to risk reduction are consistent with theory and yield estimates of VSL that are two to three times larger than estimated using the full samples. Between 2005 and 2016, estimated VSL increased sharply, from about 22,000 USD in 2005 to 550,000 USD in 2016. Income also increased substantially over this period. Attributing the change in VSL solely to the change in real income implies an income elasticity of about 3.0. Our results suggest that estimates of VSL from stated-preference studies in which WTP is not close to proportionate to the stated risk reduction may be biased downward by a factor of two or more, and that VSL is likely to grow rapidly in a population with strong economic growth, which implies that environmental-health, safety, and other policies should become increasingly protective.
Yingying Lyu. 2019. “
Walking culture in China.” Harvard Graduate School of Design.
Thesis Type: D. Des dissertation. This paper uses data from the Project's household survey in Chengdu, Sichuan.
2018
Yaowen Zhang, Ling Shao, Xudong Sun, Mengyao Han, Xueli Zhao, Jing Meng, Bo Zhang, and Han Qiao. 2018. “
Outsourcing natural resource requirements within China.” Journal of Environmental Management, 228, Pp. 292-302.
Publisher's VersionAbstractConsumption demands are final drivers for the extraction and allocation of natural resources. This paper investigates demand-driven natural resource requirements and spatial outsourcing within China in 2012 by using the latest multi-regional input-output model.
Exergy is adopted as a common metric for natural resources input. The total domestic resource exergy requirements amounted to 125.5 EJ, of which the eastern area contributed the largest share of 44.5%, followed by the western area (23.9%), the central area (23.0%) and the northeastern area (8.6%). Investment was the leading final demand category, accounting for 52.9% (66.4 EJ) of national total embodied resource use (ERU). The total trade volumes of embodied resource were equivalent to 69.6% of the total direct resource input (DRI), mostly transferred from the central and western regions such as Inner Mongolia, Shanxi, Shaanxi and Xinjiang to the eastern regions such as Jiangsu, Zhejiang, Guangdong and Shanghai. The northeastern and eastern areas had physical net imports of 1213.5 PJ and 38452.6 PJ, while the central and western inland areas had physical net exports of 6364.5 PJ and 33301.5 PJ, respectively. Shanghai, Beijing, Zhejiang, Jiangsu and Guangdong had prominent ERUs which respectively were 101.6, 12.6, 11.7, 8.4 and 4.3 times of their DRIs. The ERUs of Inner Mongolia, Shaanxi, Shanxi, Ningxia and Guizhou were equal to only 17.6%, 25.3%, 27.9%, 46.0% and 50.2% of their DRIs, respectively. Regional uneven development resulted in imbalanced resource requirements across China. The findings can provide a deep understanding of China's resource-driven economic development mode, and contribute to reducing regional resource footprints and their environment outcomes under the “new normal economy”.
Rong Ma, Bin Chen, Chenghe Guan, Jing Meng, and Bo Zhang. 2018. “
Socioeconomic determinants of China’s growing CH4 emissions.” Journal of Environmental Management, 228, 15 December 2018, Pp. 103-116.
Publisher's VersionAbstractReducing CH4 emissions is a major global challenge, owing to the world-wide rise in emissions and concentration of CH4 in the atmosphere, especially in the past decade. China has been the greatest contributor to global anthropogenic CH4 emissions for a long time, but current understanding towards its growing emissions is insufficient. This paper aims to link China's CH4 emissions during 2005–2012 to their socioeconomic determinants by combining input-output models with structural
decomposition analysis from both the consumption and income perspectives. Results show that changes in household consumption and income were the leading drivers of the CH4 growth in China, while changes in efficiency remained the strongest factor offsetting CH4 emissions. After 2007, with the
global financial crisis and economic stimulus plans, embodied emissions from exports plunged but those from capital formation increased rapidly. The enabled emissions in employee compensation increased steadily over time, whereas emissions induced from firms' net surplus decreased gradually, reflecting the reform on income distribution. In addition, at the
sectoral level, consumption and capital formation respectively were the greatest drivers of embodied CH4 emission changes from agriculture and manufacturing, while employee compensation largely determined the enabled emission changes across all industrial sectors. The growth of CH4 emissions in China was profoundly affected by the
macroeconomic situation and the changes of economic structure. Examining economic drivers of anthropogenic CH4emissions can help formulate comprehensive mitigation policies and actions associated with economic production, supply and consumption.
Zhaoxi Liu, Qiuwei Wu, Kang Ma, Mohammad Shahidehpour, Yusheng Xue, and Shaojun Huang. 2018. “
Two-stage optimal scheduling of electric vehicle charging based on transactive control.” IEEE Transactions on Smart Grid, 10, 3, Pp. 2948-2958.
Publisher's Version Chenghe Guan and Richard B. Peiser. 2018. “
Accessibility, urban form, and property value: Toward a sustainable urban spatial structure.” Journal of Transport and Land Use, 11, 1, Pp. 1057-1080.
Publisher's VersionAbstractThe effects of metro system development and urban form on housing prices highly depend on the spatial temporal conditions of urban neighborhoods. However, scholars have not yet comprehensively examined these interactions at a neighborhood-scale. This study assesses metro access, urban form, and property value at both the district- and neighborhood-level. The study area is Pudong, Shanghai, where metro system development has coincided with rapid urban growth. Two hundred and seventy-nine neighborhoods from 13 districts of Shanghai are randomly selected for the district-level investigation and 31 neighborhoods from Pudong are selected for the neighborhood-level investigation. The analysis of variance shows that metro access is more positively correlated to property price in Pudong than other districts. The Pearson correlation, principle component, and ordinary least square regression analyses show that while accessibility attributes have a positive influence on housing prices, neighborhood characteristics also exhibit a pronounced impact on property price change over time. This study extends our knowledge on how metro system development interacts with landuse efficiency and discusses planning policies that correspond to different stages of development.
Archana Dayalu, William Munger, Steven Wofsy, Yuxuan Wang, Thomas Nehrkorn, Yu Zhao, Michael McElroy, Chris Nielsen, and Kristina Luus. 2018. “
Assessing biotic contributions to CO2 fluxes in northern China using the Vegetation, Photosynthesis and Respiration Model (VPRM-CHINA) and observations from 2005 to 2009.” Biogeosciences, 15, Pp. 6713-6729.
Publisher's VersionAbstractAccurately quantifying the spatiotemporal distribution of the biological component of CO2 surface–atmosphere exchange is necessary to improve top-down constraints on China's anthropogenic CO2 emissions. We provide hourly fluxes of CO2 as net ecosystem exchange (NEE; µmol CO2 m−2 s−1) on a 0.25∘×0.25∘" id="MathJax-Element-1-Frame" role="presentation" style="position: relative;" tabindex="0">0.25∘×0.25∘ grid by adapting the Vegetation, Photosynthesis, and Respiration Model (VPRM) to the eastern half of China for the time period from 2005 to 2009; the minimal empirical parameterization of the VPRM-CHINA makes it well suited for inverse modeling approaches. This study diverges from previous VPRM applications in that it is applied at a large scale to China's ecosystems for the first time, incorporating a novel processing framework not previously applied to existing VPRM versions. In addition, the VPRM-CHINA model prescribes methods for addressing dual-cropping regions that have two separate growing-season modes applied to the same model grid cell. We evaluate the VPRM-CHINA performance during the growing season and compare to other biospheric models. We calibrate the VPRM-CHINA with ChinaFlux and FluxNet data and scale up regionally using Weather Research and Forecasting (WRF) Model v3.6.1 meteorology and MODIS surface reflectances. When combined with an anthropogenic emissions model in a Lagrangian particle transport framework, we compare the ability of VPRM-CHINA relative to an ensemble mean of global hourly flux models (NASA CMS – Carbon Monitoring System) to reproduce observations made at a site in northern China. The measurements are heavily influenced by the northern China administrative region. Modeled hourly time series using vegetation fluxes prescribed by VPRM-CHINA exhibit low bias relative to measurements during the May–September growing season. Compared to NASA CMS subset over the study region, VPRM-CHINA agrees significantly better with measurements. NASA CMS consistently underestimates regional uptake in the growing season. We find that during the peak growing season, when the heavily cropped North China Plain significantly influences measurements, VPRM-CHINA models a CO2 uptake signal comparable in magnitude to the modeled anthropogenic signal. In addition to demonstrating efficacy as a low-bias prior for top-down CO2 inventory optimization studies using ground-based measurements, high spatiotemporal resolution models such as the VPRM are critical for interpreting retrievals from global CO2 remote-sensing platforms such as OCO-2 and OCO-3 (planned). Depending on the satellite time of day and season of crossover, efforts to interpret the relative contribution of the vegetation and anthropogenic components to the measured signal are critical in key emitting regions such as northern China – where the magnitude of the vegetation CO2 signal is shown to be equivalent to the anthropogenic signal.
BG paper Michael.B. McElroy. 2018. “
Can China address air pollution and climate change.” In The China Questions: Critical Insights into a Rising Power,
edited by Jennifer Rudolph and Michael Szonyi. Cambridge: Harvard University Press.
Govinda R. Timilsina, Jing Cao, and Mun S. Ho. 2018. “
Carbon tax for achieving China's NDC: Simulations of some design features using a CGE model.” Climate Change Economics.
Publisher's VersionAbstractChina has set a goal of reducing its CO2 intensity of GDP by 60–65% from the 2005 level in 2030 as its nationally determined contribution (NDC) under the Paris Climate Change Agreement. While the government is considering series of market and nonmarket measures to achieve its target, this study assesses the economic consequences if the target were to meet through a market mechanism, carbon tax. We used a dynamic computable general equilibrium model of China for the analysis. The study shows that the level of carbon tax to achieve the NDC target would be different depending on its design features. An increasing carbon tax that starts at a small rate in 2015 and rises to a level to meet the NDC target in 2030 would cause smaller GDP loss than the carbon tax with a constant rate would do. The GDP loss due to the carbon tax would be smaller when the tax revenue is utilized to cut existing distortionary taxes than when it is transferred to households as a lump-sum rebate.
Xinyu Chen, Junling Huang, Qing Yang, Chris P. Nielsen, Dongbo Shi, and Michael B. McElroy. 2018. “
Changing carbon content of Chinese coal and implications for emissions of CO2.” Journal of Cleaner Production, 194, Pp. 150-157.
Publisher's VersionAbstractThe changing carbon content of
coal consumed in China between 2002 and 2012 is quantified using information from the power sector. The carbon content decreased by 7.7% over this interval, the decrease particularly pronounced between 2007 and 2009. Inferences with respect to the changing carbon content of coal and the oxidation rate for its consumption, combined with the recent information on coal use in China, are employed to evaluate the trend in emissions of CO
2. Emissions are estimated to have increased by 158% between 2002 and 2012, from 3.9 Gt y-1 to 9.2 Gt y-1. Our estimated emissions for 2005 are notably consistent with data reported by China in its “Second National Communication” to the UN (NDRC, 2012) and significantly higher than the estimation published recently in Nature. The difference is attributed, among other factors, to the assumption of a constant carbon content of coal in the latter study. The results indicate that CO
2 emissions of China in 2005 reported by Second National Communication are more reliable to serve as the baseline for China's future carbon commitments (e.g. those in Paris Agreement of the UNFCCC). Discrepancies between national and provincial statistics on
coal production and consumption are investigated and attributed primarily to anomalous reporting on interprovincial trade in four heavily industrialized provinces.
Bo Zhang, Xueli Zhao, Xiaofang Wu, Mengyao Han, Chenghe Guan, and Shaojie Song. 2018. “
Consumption‐based accounting of global anthropogenic CH4 emissions.” Earth's Future, 6, 9, Pp. 1349-1363.
Publisher's VersionAbstractGlobal anthropogenic CH4 emissions have witnessed a rapid increase in the last decade. However, how this increase is connected with its socioeconomic drivers has not yet been explored. In this paper, we highlight the impacts of final demand and international trade on global anthropogenic CH4 emissions based on the consumption‐based accounting principle. We find that household consumption was the largest final demand category, followed by fixed capital formation and government consumption. The position and function of nations and major economies to act on the structure and spatial patterns of global CH4 emissions were systematically clarified. Substantial geographic shifts of CH4emissions during 2000‐2012 revealed the prominent impact of international trade. In 2012, about half of global CH4 emissions were embodied in international trade, of which 77.8% were from intermediate trade and 22.2% from final trade. Mainland China was the largest exporter of embodied CH4 emissions, while the USA was the largest importer. Developed economies such as Western Europe, the USA and Japan were major net receivers of embodied emission transfer, mainly from developing countries. CH4emission footprints of nations were closely related to their human development indexes (HDIs) and per capita gross domestic products (GDPs). Our findings could help to improve current understanding of global anthropogenic CH4 emission increases, and to pinpoint regional and sectoral hotspots for possible emission mitigation in the entire supply chains from production to consumption.
Jonathan M. Moch, Eleni Dovrou, Loretta J. Mickley, Frank N. Keutsch, Yuan Cheng, Daniel J. Jacob, Jingkun Jiang, Meng Li, J. William Munger, Xiaohui Qiao, and Qiang Zhang. 2018. “
Contribution of hydroxymethane sulfonate to ambient particulate matter: A potential explanation for high particulate sulfur during severe winter haze in Beijing.” Geophysical Research Letters, 45, Pp. 11969-11979.
Publisher's VersionAbstractPM 2.5 during severe winter haze in Beijing, China, has reached levels as high as 880μg/m3, with sulfur compounds contributing significantly to PM 2.5 composition. This sulfur has been traditionally assumed to be sulfate, although atmospheric chemistry models are unable to account for such large sulfate enhancements under dim winter conditions. Using a 1-D model, we show that well-characterized but previously overlooked chemistry of aqueous-phase HCHO and S(IV) in cloud droplets to form a S(IV)-HCHO adduct, hydroxymethane sulfonate, may explain high particulate sulfur in wintertime Beijing. We also demonstrate in the laboratory that methods of ion chromatography typically used to measure ambient particulates easily misinterpret hydroxymethane sulfonate as sulfate. Our findings suggest that HCHO and not SO2 has been the limiting factor in many haze events in Beijing and that to reduce severe winter pollution in this region, policymakers may need to address HCHO sources such as transportation.
GRL paper Jaume Freire-González and Mun S. Ho. 2018. “
Environmental fiscal reform and the double dividend: evidence from a dynamic general equilibrium model.” Sustainability, 10, 2.
Publisher's VersionAbstractAn environmental fiscal reform (EFR) represents a transition of a taxation system toward one based in environmental taxation, rather than on taxation of capital, labor, or consumption. It differs from an environmental tax reform (ETR) in that an EFR also includes a reform of subsidies which counteract environmental policy. This research details different ways in which an EFR is not only possible but also a good option that provides economic and environmental benefits. We have developed a detailed dynamic CGE model examining 101 industries and commodities in Spain, with an energy and an environmental extension comprising 31 pollutant emissions, in order to simulate the economic and environmental effects of an EFR. The reform focuses on 39 industries related to the energy, water, transport and waste sectors. We simulate an increase in taxes and a reduction on subsidies for these industries and at the same time we use new revenues to reduce labor, capital and consumption taxes. All revenue recycling options provide both economic and environmental benefits, suggesting that the “double dividend” hypothesis can be achieved. After three to four years after implementing an EFR, GDP is higher than the base case, hydrocarbons consumption declines and all analyzed pollutants show a reduction.
Shaojie Song, Meng Gao, Weiqi Xu, Jingyuan Shao, Guoliang Shi, Shuxiao Wang, Yuxuan Wang, Yele Sun, and Michael McElroy. 2018. “
Fine particle pH for Beijing winter haze as inferred from different thermodynamic equilibrium models.” Atmospheric Chemistry and Physics, 18, Pp. 7423-7438.
Publisher's VersionAbstractpH is an important property of aerosol particles but is difficult to measure directly. Several studies have estimated the pH values for fine particles in North China winter haze using thermodynamic models (i.e., E-AIM and ISORROPIA) and ambient measurements. The reported pH values differ widely, ranging from close to 0 (highly acidic) to as high as 7 (neutral). In order to understand the reason for this discrepancy, we calculated pH values using these models with different assumptions with regard to model inputs and particle phase states. We find that the large discrepancy is due primarily to differences in the model assumptions adopted in previous studies. Calculations using only aerosol phase composition as inputs (i.e., reverse mode) are sensitive to the measurement errors of ionic species and inferred pH values exhibit a bimodal distribution with peaks between −2 and 2 and between 7 and 10. Calculations using total (gas plus aerosol phase) measurements as inputs (i.e., forward mode) are affected much less by the measurement errors, and results are thus superior to those obtained from the reverse mode calculations. Forward mode calculations in this and previous studies collectively indicate a moderately acidic condition (pH from about 4 to about 5) for fine particles in North China winter haze, indicating further that ammonia plays an important role in determining this property. The differences in pH predicted by the forward mode E-AIM and ISORROPIA calculations may be attributed mainly to differences in estimates of activity coefficients for hydrogen ions. The phase state assumed, which can be either stable (solid plus liquid) or metastable (only liquid), does not significantly impact pH predictions of ISORROPIA.
ACP paper Qing Yang, Hewen Zhou, Xiaoyan Zhang, Chris P. Nielsen, Jiashuo Li, Xi Lu, Haiping Yang, and Hanping Chen. 2018. “
Hybrid life-cycle assessment for energy consumption and greenhouse gas emissions of a typical biomass gasification power plant in China.” Journal of Cleaner Production, 205, Pp. 661-671.
Publisher's VersionAbstractAmong biomass energy technologies which are treated as the promising way to mitigate critical energy crisis and global climate change, biomass gasification plays a key role given to its gaseous fuels especially syngas for distributed power plant. However, a system analysis for the energy saving and greenhouse gas emissions abatement potentials of gasification system has been directed few attentions. This study presents a system analysis that combines process and input-output analyses of GHG emissions and energy costs throughout the full chain of activities associated with biomass gasification. Incorporating agricultural production, industrial process and wastewater treatment which is always ignored, the energy inputs in life cycle are accounted for the first commercial biomass gasification power plant in China. Results show that the non-renewable energy cost and GHG emission intensity of the biomass gasification system are 0.163 MJ/MJ and 0.137 kg CO2-eq/MJ respectively, which reaffirm its advantages over coal-fired power plants in clean energy and environmental terms. Compared with other biomass energy processes, gasification performs well as its non-renewable energy cost and CO2 intensity are in the central ranges of those for all of these technologies. Construction of the plant is an important factor in the process’s non-renewable energy consumption, contributing about 44.48% of total energy use. Wastewater treatment is the main contributor to GHG emissions. The biomass gasification and associated wastewater treatment technologies have critical influence on the sustainability and renewability of biomass gasification. The results provide comprehensive analysis for biomass gasification performance and technology improvement potential in regulating biomass development policies for aiming to achieve sustainability globally.
Meng Gao, Gufran Beig, Shaojie Song, Hongliang Zhang, Jianlin Hu, Qi Ying, Fengchao Liang, Yang Liu, Haikun Wang, Xiao Lu, Tong Zhu, Gregory Carmichael, Chris P. Nielsen, and Michael B. McElroy. 2018. “
The Impact of Power Generation Emissions on Ambient PM2.5 Pollution and Human Health in China and India.” Environment International, 121, Part 1, Pp. 250-259.
Publisher's VersionAbstract
Emissions from power plants in China and India contain a myriad of fine particulate matter (PM2.5, PM≤2.5 micrometers in diameter) precursors, posing significant health risks among large, densely settled populations. Studies isolating the contributions of various source classes and geographic regions are limited in China and India, but such information could be helpful for policy makers attempting to identify efficient mitigation strategies. We quantified the impact of power generation emissions on annual mean PM2.5 concentrations using the state-of-the-art atmospheric chemistry model WRF-Chem (Weather Research Forecasting model coupled with Chemistry) in China and India. Evaluations using nationwide surface measurements show the model performs reasonably well. We calculated province-specific annual changes in mortality and life expectancy due to power generation emissions generated PM2.5 using the Integrated Exposure Response (IER) model, recently updated IER parameters from Global Burden of Disease (GBD) 2015, population data, and the World Health Organization (WHO) life tables for China and India. We estimate that 15 million (95% Confidence Interval (CI): 10 to 21 million) years of life lost can be avoided in China each year and 11 million (95% CI: 7 to 15 million) in India by eliminating power generation emissions. Priorities in upgrading existing power generating technologies should be given to Shandong, Henan, and Sichuan provinces in China, and Uttar Pradesh state in India due to their dominant contributions to the current health risks.
Xinyu Chen, Zhiwei Xu, Chris P Nielsen, and Michael B. McElroy. 2018. “
Impacts of fleet types and charging modes for electric vehicles on emissions under different penetrations of wind power.” Nature Energy, 3, Pp. 413-421.
Publisher's VersionAbstractCurrent Chinese policy promotes the development of both electricity-propelled vehicles and carbon-free sources of power. Concern has been expressed that electric vehicles on average may emit more CO2 and conventional pollutants in China. Here, we explore the environmental implications of investments in different types of electric vehicle (public buses, taxis and private light-duty vehicles) and different modes (fast or slow) for charging under a range of different wind penetration levels. To do this, we take Beijing in 2020 as a case study and employ hourly simulation of vehicle charging behaviour and power system operation. Assuming the slow-charging option, we find that investments in electric private light-duty vehicles can result in an effective reduction in the emission of CO2 at several levels of wind penetration. The fast-charging option, however, is counter-productive. Electrifying buses and taxis offers the most effective option to reduce emissions of NO x , a major precursor for air pollution.
Xiaolin Guo, Mun Sing Ho, Liangzhi You, Jing Cao, Yu Fang, Taotao Tu, and Yang Hong. 2018. “
Industrial Water Pollution Discharge Taxes in China: A Multi-Sector Dynamic Analysis.” Water, 10, 12, Pp. 1742.
Publisher's VersionAbstractWe explore how water pollution policy reforms in China could reduce industrial wastewater pollution with minimum adverse impact on GDP growth. We use a multi-sector dynamic Computable General Equilibrium (CGE) model, jointly developed by Harvard University and Tsinghua University, to examine the long-term impact of pollution taxes. A firm-level dataset of wastewater and COD discharge is compiled and aggregated to provide COD-intensities for 22 industrial sectors. We simulated the impact of 4 different sets of Pigovian taxes on the output of these industrial sectors, where the tax rate depends on the COD-output intensity. In the baseline low rate of COD tax, COD discharge is projected to rise from 36 million tons in 2018 to 48 million in 2030, while GDP grows at 6.9% per year. We find that raising the COD tax by 8 times will lower COD discharge by 1.6% by 2030, while a high 20-times tax will cut it by 4.0%. The most COD-intensive sectors—textile goods, apparel, and food products—have the biggest reduction in output and emissions. The additional tax revenue is recycled by cutting existing taxes, including taxes on profits, leading to higher investment. This shift from consumption to investment leads to a slightly higher GDP over time.
