Final Demand Structure Research Articles

Quantitative assessment of PM2.5-related human health impacts at the provincial level in China and analysis of its heterogeneity affected by economic structural transformation

Rapid economic development has led to massive fossil energy consumption and emissions of air pollutants such as PM2.5, which have severely impacted human health and the environment. By uncovering the primary regions and pivotal sectors of PM2.5-related human health impacts (PM2.5-HHI) and evaluating the influence of economic structural factors on them, we can facilitate a more targeted strategy for managing PM2.5 pollution sources. This study employs a structural decomposition analysis method based on input–output analysis to evaluate the impact of China’s provincial economic structural transformation and changes in final demand on PM2.5-HHI in the years 2012, 2015, and 2017. Results indicated that PM2.5-HHI is primarily concentrated in economically developed provinces (e.g., Shandong and Guangdong), which is compared to Shanghai, Heilongjiang, Liaoning, and Hebei experienced negative growth in PM2.5-HHI during 2007–2017. The production-based PM2.5-HHI is primarily driven by energy-intensive sectors such as the production and distribution of electric power and heat power. By contrast, the building sector is key to driving consumption-based PM2.5-HHI. An increasing number of regions are reducing PM2.5-HHI by implementing production structure changes. Moreover, the driving effect of production structure changes on PM2.5-HHI growth is strengthening in Beijing and Tianjin. Changes in the final demand structure mainly led to the growth of PM2.5-HHI in areas with higher economic development levels, such as Beijing and Shandong, but this driving effect is weakening. The final demand–driven PM2.5-HHI shows an evolutionary trend of an increasing share driven by fixed capital formation and exports and a decreasing share driven by household consumption. Changes in emission intensity play a key role in decreasing PM2.5-HHI in each region. Alternatively, changes in the structure of emission sources have a relatively minor impact on PM2.5-HHI. To mitigate PM2.5-HHI, regional economic and resource endowment advantages should be used to promote regional coordinated development and strengthen green production-process innovation in energy-intensive industries. Meanwhcile, it is necessary to optimize urban construction planning and improve the energy efficiency of buildings.

Synergies quantification and evolution on air pollutant and CO2 emissions driven by different socio-economic effects

Realizing a synergistic reduction of air pollutant and CO2 emissions (APCE) is an important approach to promote a green socio-economic transformation in China, and it can provide a solid foundation for the achievement of clean energy production and climate action under a sustainable development goal framework. The objective of this study is to explore the quantitative relationship and evolution of synergies between APCE in industrial sectors driven by different socio-economic effects from 2007 to 2020 in China. The results indicated that the main sectors of pollutant emissions had consistency, however, large differences in the reduction efficiency of emissions exist among pollutants. The efficiency in reducing CO2 emissions was about 48% lower when compared with reductions of SO2 (95%), NOx (86%), and smoke and dust (83%) emissions from 2007 to 2020. The effects of improved technology were the main contributor to a reduction in pollutant emissions, but the synergies between APCE driving by it were not achieved. While the synergies between APCE driven by structure and final demand effects were significant. The synergies between NOx and CO2 emissions were stronger driven by final demand structure and type effects, with correlation coefficients of 1.06 and 1.13, respectively. Besides, the degree of synergistic reduction between APCE in most industrial sectors was around zero. Therefore, the efficiency of synergistic pollution reduction should be improved with the development of a synergistic governance system for industrial sectors. The structural decomposition analysis based on input-output model combined with the cross-elasticity analysis method to quantitively synergies between APCE from the consumption (demand) perspective, considering the connections between industrial sectors with socio-economic developing, which would contribute to the industrial synergistic reduction and green transformation as the consumption driven gradually increasing.

How to improve the energy-saving performance of China's transport sector? An input–output perspective

The transport sector proves a major energy consumer in China, but improving energy-saving performance in China's provincial transport sector from the lifecycle perspective remains unresolved. Thus, this study employs the environmentally extended multi-region input–output (MRIO) method, structural path analysis, and the newest MRIO table of China from 2017, to investigate how to improve the energy-saving performance from final demand structure, supply chain, and pathway perspectives. The relevant results are threefold. (1) Regarding the final demand structure level, the embodied energy consumption of China's transport sector is predominantly driven by investment from the production side, while that of the consumption side is primarily caused by exports. (2) At the supply chain level, production-side embodied energy consumption primarily occurs along a three-echelon supply chain, while that from the consumption side mostly occurs via a two-echelon supply chain. (3) At the pathway level, the production-side energy-saving performance of China's provincial transport sector is dominated by two pathways along the construction sector, including transport sector → construction sector → final demands, and transport sector → intermediate inputs → construction sector → final demands, while that of the consumption side is chiefly determined by three pathways along internal transportation chains.

What drives water conservation in the supply chain of the Yellow River Basin? An empirical analysis based on SPD.

Industrial water saving is an objective requirement for the high-quality development of the Yellow River Basin, as water resource is the largest rigid constraint. In this study, water resources input-output model, structural decomposition analysis (SDA) and structural path analysis (SPA) were constructed to decompose the driving factors of total water use in typical water-deficient provinces (Ningxia, Shanxi, and Henan) in China's Yellow River Basin, to calculate their water use at each production stage and identify their key water-saving pathways. The results were as follows: (i) Water intensity had the most obvious impact on total water saving, resulting in efficiency improvements of 81.39%, 9.21%, and 78.45% for each province, respectively. The next factor was the final demand structure, which suppressed total water-saving efforts by 24.23%, 11.52%, and 113.12% in the respective provinces. (ii) The key water-saving paths in the typical water-deficient provinces of the Yellow River Basin were primarily centered around Sector 1. (iii) Water intensity had a strong water-saving effect on the key paths in the three provinces, with contribution rates of 100.42%, 59.02%, and 42.34% for Ningxia, Henan, and Shanxi, respectively. Final demand also contributed to water-saving in the key paths of Shanxi and Henan, with contribution rates of 35.06% and 28.23%, respectively. However, it inhibited water-saving efforts in the key paths of Ningxia, reducing it by 8.64%. Policy measures should be tailored to local conditions.

Driving Factors of Carbon Emissions in G20 Countries: A Weighted Average Structural Decomposition Approach.

As a major part of global development governance, G20 countries account for 80% of global carbon emissions. In order to achieve the goal of "carbon neutrality" proposed by the United Nations, it is important to compare and analyze the drivers of carbon emissions in G20 countries and provide recommendations for their carbon emission reduction. Based on the data of 17 G20 countries in the EORA database, this paper compares the drivers of carbon emissions of each country from 1990-2021 using weighted average structural decomposition and K-mean model. This paper focuses on four drivers, including carbon emission intensity, final demand structure, export structure, and production structure. Carbon emission intensity and final demand structure are the main factors of carbon emission reduction, and the other two factors have little influence. Among the G20 countries, the UK is in the first category because it does the best job on the four factors of carbon emissions, yet Italy is in the last category because it does not take full advantage of the four factors. Therefore, improving supply energy efficiency and adjusting demand, export, and industrial structure have become important tools for countries to transform and achieve carbon neutrality.

Structural decomposition analysis of Brazilian greenhouse gas emissions

This paper aims to verify the long run structural changes in Greenhouse Gas (GHG) emissions in Brazil which presents the particularity of high emissions in the agriculture and livestock sectors, and low emissions in energy sectors. We use an additive Structural Decomposition Analysis (SDA) to verify the sectorial changes in emissions derived from the effects of economic intensity, final demand structure effect and technological change by component i.e., household consumption, exports, gross fixed capital formation, government consumption, inventory change and total final demand. The database is composed of a series of input-output matrices at constant prices for the period 2000–2017, with comparable production structure of 42 sectors. Our results show that agriculture had the most remarkable effects on emissions, followed by industry sectors for all SDA effects and components except for inventory change. Finally, we discuss relationships between energy, GHG emissions and production structure towards development policies to reduce emissions and improve energy use.

Unstable decoupling of CO2 emissions from sectoral economic growth calls for decarbonization policies based on multi-perspective accounting: a case study of Zhejiang, China.

Decoupling CO2 emissions from economic growth is an important cornerstone of a country's decarbonization policy. Existing research in this avenue has mainly focused on decoupling the production-based (PB) CO2 emissions from economic growth at the national level, while decoupling the consumption-based (CB) and income-based (IB) CO2 emissions from economic growth, especially at the sectoral level, has received less attention. Using China's Zhejiang province as a case study, we conduct one of the first studies focused on decoupling the PB, CB, and IB CO2 emissions from economic growth at the sectoral level. Our results reveal that (1) during 2002-2017, the sectoral decoupling level varies greatly among the three different perspectives; (2) most of the examined sectors were in unstable decoupling states while some sectors even reverted to coupling states; and (3) the drivers of CO2 emissions at the sectoral level reveal important factors, such as emissions intensity, production structure, and final demand structure, that contribute toward decarbonization. Our study demonstrates to policymakers how utilizing a multi-perspective evaluation of the decoupling of emissions from sectoral economic growth can improve the accuracy of decarbonization policies and identify critical sectors toward CO2 reduction objectives.

ANALYSIS OF CHANGES IN THE INPUT OUTPUT STRUCTURE OF DKI JAKARTA PROVINCE IN 2016 AND 2024: USING THE DYNAMIC INPUT OUTPUT (DIO) MODEL

This study aims to look at the estimated structural changes in the input output table for DKI Jakarta Province in 2024. Estimates for changes in the structure are obtained by making projections of the input-output table for DKI Jakarta Province in 2024 using the Dynamic Input Output (DIO) model, namely by embedding the econometric model into the input output model, using the 2016 based year, 52 economic sectors, and 22 data series from 2000 to 2021.DIO is a hybrid model that places more emphasis on non-survey aspects that combines macro-econometric equations with identity equations in input-output analysis. This model has many dynamic equations consisting of 425 equation model. The parameters value of the equation are estimated using a combination of three estimation methods: (1) Ordinary Least Square, (2) First Order of Autoregressive, and (3) Second Order of Autoregressive. The general balance value in the DIO model is formulated using the Gauss-Seidel iteration "RAS" method.The results of the study show that in 2024 the corporate services sector, the chemical, pharmaceutical and traditional medicine industry sector, and the transportation equipment industry sector are the dominant sectors in shaping changes in the structure of intermediate demand. The Construction Sector, the Corporate Services sector, and the transportation equipment industry sector are the dominant sectors shaping changes in the structure of intermediate input. The construction sector, the real estate sector, and the wholesale and retail trade sector, not cars and motorcycles, are the dominant sectors in shaping changes in the structure of final demand. For the gross value added component, the Wholesale and Retail Trade sector, Not Cars and Motorcycles, the Private Information and Communication sector, and the Financial Intermediary Services sector other than the Central Bank are the largest sectors in the formation of structural changes.

The statistical projection of global GHG emissions from a consumption perspective

Projections of future emissions based on current trends are vital to assess the performance of mitigation efforts and to devise mitigation strategies. However, previous studies are uncertain how the persistence and convergence of mitigation efforts may impact future trajectories from the consumption perspective. Here we assume regional convergence over time after finding strong supporting historical evidence and develop a Markov Chain Monte Carlo (MCMC) Bayesian analysis of historical and projected consumption-based emissions during 1995–2050 via a modified Kaya identity. We find global emissions of 21–94 GtCO 2 eq yr −1 by 2050, slightly higher than SSP1-2.6 and encompassing SSP3-7.0, resulting in 1.4–2.9 °C of warming above pre-industrial levels mid-way through the century. The median projection shows a 2028 peak of 42 GtCO 2 eq yr −1 and an annual average decrease of 0.25 GtCO 2 eq thereafter. Future emission decreases result from the interplay between a rapid reduction driven by decreasing consumption-based emission intensities, especially in clothing and transport, and the change of final demand structure (−1.53 GtCO 2 eq yr −1 ) against increases from economic and population growth (+1.33 GtCO 2 eq yr −1 ). This highlights the importance in continually updating projections and considering models that reflect convergence and consumption structure changes, in order to better inform climate policy.

Decomposing manufacturing CO2 emission changes: An improved production-theoretical decomposition analysis based on industrial linkage theory

Identifying key drivers of manufacturing CO2 emissions is critical to carbon reduction practices. For manufacturing, CO2 emissions are mainly determined by production capacity and production scale. However, traditional production-theoretical decomposition analysis (PDA) fails to consider production-scale-related drivers. To better support policy development and implementation, this paper improves PDA based on industrial linkage theory. The improved model can identify seven production-capacity-related drivers and five production-scale-related drivers, allowing a comprehensive understanding of CO2 emission drivers. Then this model is implemented to investigate CO2 emission changes in 18 manufacturing sectors in Hubei Province, China, from 2012 to 2017. Results show that manufacturing CO2 reduction efforts in Hubei Province have yielded some achievements, with reduced potential energy intensity and improved CO2 emission technical efficiency in most sectors. Changes in external market demand and final demand structure have contributed to CO2 reduction in most sectors. Results also reveal some problems in manufacturing in Hubei Province, such as the inability to improve CO2 emission technical efficiency and CO2 emission technology strength, the slow improvement of energy utilization technical efficiency and energy utilization technology strength, and the reduction of value-added rate.

Changes of production and consumption structures in coastal regions lead to mercury emission control in China

AbstractChina is important in the global mercury (Hg) cycle and is experiencing substantial economic structure transitions. There are pronounced differences in economic development, industrial structure, and consumption patterns across regions in China. However, the impacts of regional economic structure transitions (i.e., production and final demand structures) on Hg emissions in China remain unknown. Here, we reveal the transboundary impacts of changes in regional economic structures on provincial Hg emissions in China. We found that the transitions of production and final demand structures in coastal regions led to Hg emission reductions in China during 2007–2017. In particular, production structure changes in East Coast contributed to 36 metric tons of national Hg emission reduction, where 28 metric tons occurred in other regions (especially Hebei). Its final demand structure transition contributed to 19 metric tons of national emission reduction, where 15 metric tons occurred in other regions (especially Henan). Unfortunately, production structure changes in Northwest and final demand structure changes in Southwest contributed to Hg emission increments in China during 2007–2017. For instance, changes in the final demand structure of Southwest caused 34 metric tons of emission increments, mainly from provinces within the region. Thus, spatially explicit measures for China's Hg emission control can focus on the optimizations of production structure in Northwest and final demand structure in Southwest, as well as the promotion of interregional joint actions between East Coast and North China (especially Hebei and Henan). The findings of this study can inform region‐specific policy decisions and interregional joint efforts to control Hg emissions around the world.

What caused the wage changes in tourism-related industries? A demand-side analysis based on an extended input-output model

ABSTRACT Previous studies that have explored the factors influencing wage changes in tourism have generally been confined to supply-side analysis, ignoring the impact of final demand on wage changes through the mechanism of inter-industry linkages. To this end, this study proposes a novel framework that combines input-output analysis with the logarithmic mean Divisia index method to examine wage levels and their changes in tourism-related industries and to clarify how final demand affects wage changes. The empirical results for China reveal significant wage differences among tourism-related industries, with accommodation and catering sectors consistently exhibiting a low-wage profile. In addition, the decomposition results show that the wage changes from 2002 to 2017 were mainly caused by the positive role of the total final demand effect and the negative role of the labour compensation coefficient effect. The final demand structure effect and Leontief structural effect are potential drivers of wage growth.

Energy use and energy-related carbon dioxide emissions drivers in Egypt's economy: Focus on the agricultural sector with a structural decomposition analysis

Investigating drivers of energy use and its associated CO2 emissions changes is important for sustainable development. Using input-output structural decomposition analysis, this paper decomposed the energy use and resulting CO2 emissions drivers of changes in Egypt's wide economy, particularly in its agricultural sector, between 1972 and 2014. Fuel sources dominating energy use and energy-related CO2 emissions in the agriculture sector were explicitly identified. Affluence and demographic effects were the dominant drivers at both investigation levels. Household and government final consumption changes and exports expansion were important determinants behind changes in energy use and resulting CO2 emissions at the aggregate level from the final demand destination perspective. In the agricultural sector, petroleum and oil were the dominant fuel types. The technological effect was the main driver restraining energy use growth at the aggregate level. However, technological improvements have not been sufficient to make up for the evolving economy and consumption levels. Conversely, technological changes adversely impacted energy-related CO2 emissions at the economy-wide level, and both considered environmental indicators in the agricultural sector. Overall, upgrading technology and improving the final demand structure will be critical for Egypt to promote decoupling in the future.

Structural decomposition of global co2 emissions changes and contributions of the United States, European Union, BRIC and Rest of the World

The aim of this study was to estimate the structural decomposition of the variation of the world CO2 emissions with the use of the world's input-output matrix in four effects: intensity, technology, final demand structure and volume of final demand. The main conclusions are that the intensity effect was the main mitigating factor of carbon dioxide emissions globally (-8202 GtCO2). The main factor responsible for the increase in emissions was the volume effect of final demand (economic growth) with 9843 GtCO2, followed by structural effect of the final demand with 7257 GtCO2 and then the technology effect with 1152 GtCO2. The United States and the European Union have reduced total emissions, the first mainly by technology effect and the second by intensity effect. The BRIC countries and Rest of the world made efforts to mitigate emissions by intensity and technology effects, however, the positive values of volume and structure effects of the final demand outweighed the negative values resulting in increased emissions. Therefore, targets and strategies for mitigating carbon dioxide emissions should consider the development stage of countries and the development of sustainable lifestyles and conscious consumption are important for new emission mitigation strategies.

How does global transport sector improve the emissions reduction performance? A demand-side analysis

Transport sector is one of the main global carbon emitters. Its production-side aggregated carbon intensity (i.e., the ratio of carbon emissions to GDP, AI) has been well explored, but its demand-side aggregated embodied carbon intensity (i.e., the ratio of embodied emissions to embodied value added, AEI) is always ignored, which is not conducive to improving its demand-side emissions reduction performance. Thus, using the latest world input–output table from EXIOBASE, this paper adopts the environmentally extended multi-regional input–output model and the structural path analysis model to investigate this issue from the views of globe, region, transmission layer, and final demand structure, respectively. The results indicate that: first, the AEI and AI of global transport sector contribute to global AI with 4.2% and 6.7%, respectively. The AI of global transport sector is chiefly contributed by water transport and air transport, followed by land transport, while its AEI is mainly contributed by land transport, followed by water transport and air transport. Second, transport sector highly contributing to regional AEI are mostly from the developed regions in Europe. Land transport, water transport, and air transport highly contributing to the AEI of regional transport sector are generally from the regions with low AEI of transport sector, the coastal regions, and the developed regions in Europe, respectively. Finally, the critical transmission layers of the AEI of regional transport sector have great differences in various regions, but their critical final demands are similar.

Environmental implications of economic transformation in China's Pearl River Delta region: Dynamics at four nested geographical scales over 1987–2017

Environmental pollution in the Pearl River Delta (PRD) region is largely driven by socioeconomic forces outside the region as vast majority of manufacturing products produced in the region are destined to national and international markets. Given the remarkable economic transformation of the PRD in the past decades, this study investigates the impacts of local, provincial, national, and global socio-economic drivers on PRD's pollution dynamics under the background of significant economic restructuring and upgrading from 1987 to 2017. The results indicate that changes in pollution pattern were deeply shaped by the economic transformation. The share of PRD's emissions driven by international exports expanded significantly before 2007 as a result of the fast growth of international exports. The transformation of economic growth to domestic consumption driven model since the 2007–2008 global financial crisis had resulted in an increasing contribution share to the PRD's environmental pollution from local demand and trade with Rest of China (RoC). Similarly, as final demand structure evolving towards the high value-added manufacturing and services, the share of emissions driven by low value-added manufacturing (LVM) demand had decreased by an enlarged margin, while that driven by high value-added manufacturing (HVM) demand and services demand had moved in the opposite direction. The structural decomposition analysis shows that reduction in emission intensity remains the most effective way in pollution alleviation. The contribution of changes in production input structure also shifted from a strong impetus force before 2007 to a mitigating force afterwards due to significant technological progresses in the industrial sectors since the global financial crisis. With the marginal cost of reducing emission intensity becoming prohibitively expensive, the optimization of production structure and consumption pattern is likely to play more important role in future emission mitigation.

Global Economic Structure Transition Boosts Atmospheric Mercury Emissions in China

AbstractAs the “world factory,” China has the most massive anthropogenic atmospheric mercury (Hg) emissions in the world along with its intense production activities. The development of global society and economy, including technology development, consumption behavior evolution, population growth, and so on, would significantly affect China's production and thus the Hg emissions. Here we uncover critical global socioeconomic drivers of atmospheric Hg emission changes in China during 2005–2015. Results show that the transition of global economic structures (i.e., production and final demand structures) caused 179 tons of increase in China's atmospheric Hg emissions during this period if other factors remained constant, while the transition of foreign economic structures contributed as much as 83% (149 tons). More importantly, for the first time, we reveal the crucial role of production structure transition in foreign nations. The cumulative effect of foreign production structure transition during this period (93 tons) was larger than that of foreign final demand structure transition (56 tons). It is also far more extensive than the cumulative effect of China's production structure transition (9.5 tons). Previous studies on China's atmospheric Hg emissions driven by global final demand focused more on optimizing consumption behaviors. However, this study proves the significance of a greener global production structure for reducing anthropogenic atmospheric Hg emissions in China, thus implementing the Minamata Convention on Mercury.

Moving in the Right Direction: Consumption-Based Projection Shows an Earlier and Lower Peak in Global Anthropogenic GHG Emissions

Projections of future emissions based on current trends are vital to assess the performance of mitigation efforts and to devise mitigation strategies. Several studies have investigated the aspiration gap between projected emissions and global warming targets, but it is as yet uncertain how changes in consumption patterns may impact trajectories. Here we employ a Markov Chain Monte Carlo (MCMC) Bayesian analysis of historical and projected consumption-based emissions during 1995-2050 via a modified Kaya identity. We assume regional convergence over time after finding strong supporting historical evidence. We find global emissions of 27-62 GtCO2eq yr-1 by 2050, slightly higher than SSP1-2.6 and encompassing SSP4-6.0, resulting in 1.9-2.4°C of warming above pre-industrial levels mid-way through the century. The median projection shows a 2024 peak of 55 GtCO2eq yr-1 and an annual average decrease of 0.58 GtCO2eq thereafter. Future emission decreases result from the interplay between a rapid reduction driven by decreasing emission intensities, especially in housing, and the change of final demand structure (-53 GtCO2eq yr-1) against increases from economic and population growth (+44 GtCO2eq yr-1). Compared to other work, we find an earlier peak which is lower in magnitude. This is due to the inclusion of recent data, the consideration of regional convergence, and the incorporation of structural changes in final demand as explanatory variables. This highlights the importance in continually updating projections and considering models that reflect convergence and demand structure changes, in order to better inform climate policy.

Effects of economic structural transition on PM2.5-Related Human Health Impacts in China

China has been experiencing significant economic structural transition and PM2.5-Related Human Health Impacts (PM2.5-HHI). However, the effects of economic structural transition on PM2.5-HHI in China remain unclear. Our study analyzes the relative contributions of the changes in economic structural factors (including production structure, final demand structure, and emission source structure) to changes in China’s PM2.5-HHI during 1990–2015, using the input-output based structural decomposition analysis. Results show that the change in production structure generally has contributed to reducing PM2.5-HHI since 2009. The change in final demand structure leads to an increase in PM2.5-HHI. Moreover, the driving effect of fixed capital formation on the increase in PM2.5-HHI is significantly enhanced. The change in emission source structure has contributed to reducing PM2.5-HHI since 1995, mainly due to the optimization of energy use structures. Our findings can provide scientific supports for economic structural regulations to reduce PM2.5-HHI in China. Besides strengthening the end-of-pipe PM2.5 emission control of key sectors, special attention should be paid to the structural factors optimization that have great potentials of reducing PM2.5-HHI.

Consumption- and Income-Based Sectoral Emissions of Polycyclic Aromatic Hydrocarbons in China from 2002 to 2017.

China is the largest emitter of polycyclic aromatic hydrocarbons (PAHs) in the world. Because of their negative influences on human health, the characteristics and potential driving forces of PAH emissions should be evaluated to establish effective mitigation strategies for different economic sectors. This study is the first to quantify the embodied and enabled PAH emissions of 108 sectors in China in 2002, 2007, 2012, and 2017. The results show that the total sectoral emissions increased by 92% (from 28.0 to 53.7 kt) from 2002 to 2012 and decreased to 53.0 kt in 2017. The eight aggregated sectors had different characteristics according to their production-, consumption-, and income-based emissions. Both the embodied and enabled emission flows increased (from 13.8 to 29.2 kt and from 11.3 to 20.5 kt, respectively) with time. The influences of the major final demands and primary inputs reversed from increasing to decreasing emissions over time. In particular, the primary input structure had a stronger impact on decreasing emissions than the final demand structure. The results revealed that different mitigation policies should be applied to different sectors and that adjusting primary input behaviors might be a relatively efficient method to reduce PAH emissions.