Output Structural Decomposition Analysis Research Articles

A holistic analysis of China's consumption-based water footprint (2012–2017) from a multilevel perspective

As the world's second-largest economy, rapid socioeconomic development in China has exerted unprecedented pressure on water resources. However, there is still uncertainty regarding consumption-based water footprint (CWF) assessment across different spatiotemporal scales, especially those differentiated by green, blue, and grey water footprints. To address this knowledge gap, this study employed a multi-regional input–output (MRIO) model and structural decomposition analysis (SDA) to quantify the spatiotemporal dynamics and key drivers of consumption-based green (GCWF), blue (BCWF), and grey (HCWF) water footprints in mainland China during 2012–2017. The main findings of the study are as follows. (1) The HCWF is approximately 4.4 times greater than the sum of the GCWF and BCWF, with water consumption caused by pollution significantly exceeding direct consumption. However, there was a significant decrease in all three CWFs after 2015, particularly HCWF. (2) The spatial distribution pattern of the CWF shows high values in the east and south and low values in the west and north, while virtual water (VW) transfer through commodity trade occurs mainly from inland to coastal regions. (3) The three highest water-consuming sectors were agriculture, food and tobacco processing, and construction, accounting for over 70% of the total water consumption. (4) Water efficiency and consumption patterns are crucial factors in reducing CWF, while affluence levels have the greatest impact on increasing CWF, followed by changes in industrial structure after 2015. Overall, the results of this study contribute to a comprehensive understanding of China's regional and sectoral water consumption throughout the supply chain. In addition, our findings identified regions and sectors with high water consumption and their drivers, which can inform policy decisions and contribute to synergistic cross-regional regulation and sustainable water resources management.

Exploring Water Use in Mildly Water-Scarce Regions: A Structural Decomposition Analysis for Anhui Province in China

Because of severe water scarcity, it is crucial to quantify water use in China’s water-scarce regions and to analyze the factors driving its use. In this paper, we analyze the driving factors behind the water footprint from 2007 to 2017 in the mildly water-scarce region of Anhui Province, using regional input–output models and structural decomposition analysis. The results show the following: (1) Although the average virtual water intensity of Anhui Province decreased by 52.58% from 2007 to 2017, the water-use efficiency was still low; (2) During the study period, the total water footprint of Anhui Province increased by 25.06%, with an obvious upward trend; (3) From 2012 to 2017, the net export volume of virtual water in Anhui Province was positive, and the flow volume decreased by 78.87%; (4) Technological progress contributed the most to reducing the water footprint of Anhui Province, followed by the increased counter-urbanization rate as the second-largest driving force. The expansion of the scale effect, including the size of the economy and the size of the population, was the main negative driving force for the increase in Anhui’s water footprint. However, the proportion of economic scale in the water footprint drivers gradually decreased.

Decarbonization in New Zealand – where and how: a combination of input–output approach and structural decomposition analysis

In New Zealand, agriculture accounts for around 50% of total gross greenhouse gas emissions, energy production and use account for 40%. Given the limited possibility of reducing methane from the agricultural sector, we focus on the shape of, and factors influencing, the energy-related emissions profile. A single framework that combines environmental input–output analysis with structural decomposition analysis is established to systematically investigate driving forces shaping energy-related greenhouse gas emissions at national and sectoral levels. Results show that emission intensity, population, and consumption volume are dominant factors influencing shifting the energy-related emissions profile from 2007 to 2013. To achieve a net-zero-carbon economy, decarbonizing the transport sector is foremost and should be given priority. Policy directed at improving energy utilization in private consumption and exports, and the adoption and utilization of advanced technology in manufacturing, would contribute to achieving emissions reduction targets.

Multiregional input–output analysis of carbon transfer in interprovincial trade and sectoral strategies for mitigation: Case study of Shanxi province in China

Embodied carbon emissions have an important role in China's “dual carbon” target and carbon reduction policy. Our study applied a multiregional input–output (MRIO) model and structural decomposition analysis (SDA) to examine the spatiotemporal evolution and driving forces of embodied carbon emissions in the specific region of Shanxi Province, an energy-rich region in China, in the empirical study using MRIO data. First, we found that Shanxi had considerable net embodied carbon outflow in interprovincial trade, with outflow increasing from 216.18 Mt in 2012 to 221.60 Mt in 2017. Second, the main driving forces of embodied carbon outflow include technological level and trade demand. We then classified 29 sectors into six categories, including mainly developed, controlled, moderately guided, moderately controlled, cost guided, and maintained to describe the relationship between the net transfer of embodied carbon emissions and value added, and formulated proposed sectoral development policies. Third, the sectors with the highest net embodied carbon outflow include electricity and heat production and supply; metal smelting and rolling processing; and petroleum processing, coking, and nuclear fuel processing. We constructed a coupled relationship model to adjust the technology and scale of interprovincial trade to reduce carbon emissions with lower cost and higher equity. The results can be used as a reference for formulating carbon reduction policies in energy-rich regions.

Assessing the embodied CO2 emissions of ICT industry and its mitigation pathways under sustainable development: A global case

Considering the rapid development of the global digital industry and the need for sustainable development and climate change, this paper explores the global ICT-embodied CO2 emissions changes and intensities from 2000 to 2014, and the driving factors behind ICT-embodied CO2 emissions changes in 15 countries by using the multi-regional input–output (MRIO) method and structural decomposition analysis (SDA) method. We also use Monte Carlo simulations to forecast the ICT-embodied CO2 emissions from 2020 to 2050. The results indicate that: (1) After 2009, global ICT’s embodied CO2 emissions first decline and then increase, and the sector that manufactures computer, electronic, and optical products account for 82.83% of the global ICT’s embodied CO2 emissions in 2014; (2) judging from the emission-reduction trends of various countries, CO2 emissions intensity factors are the main drivers restricting the growth of embodied CO2 emissions in the ICT sector, while final demand composition factors play a significant role in promoting the increase of ICT’s embodied CO2 emissions; (3) Australia, Germany, and Indonesia contribute to an increase in ICT’s embodied CO2 emissions due to sectoral heterogeneity, while Canada, China, Germany, and South Korea have contributed to an increase in ICT’s embodied CO2 emissions in terms of regional heterogeneity; (4) the drivers have also changed significantly with the impact of the financial crisis; and (5) with the current pattern of development, it is difficult to reach the peak of ICT-embodied CO2 emissions in most countries before 2050. This paper also provides feasible implementation plans for carbon mitigation in the global ICT industry.

Transfer patterns and driving factors of China’s energy use in trade: Evidence from multiregional input–output analysis and structural decomposition analysis

China consumes a large amount of fossil fuels to support its economic development, making it the world’s largest carbon emitter. To provide a solid basis for relevant policies and measures for energy conservation and emissions reduction, the evolution patterns of China’s energy use in trade were detailed analyzed in this article. The results showed that the ratio of net embodied energy flows to total energy consumption declined during the investigated period. The highly developed areas (such as Jiangsu, Zhejiang and Guangdong) imported embodied energy, while the least developed provinces (e.g., Guizhou and Yunnan) and areas with heavy industries (Hebei, Inner Mongolia and Shanxi) exported embodied energy. In terms of decomposition results, energy coefficients and per capita demand played dominant roles in determining energy consumption changes. Only the changes in the production trade structure represented obvious and continued negative spillover effects on energy decreases. The highly and least developed regions showed a reverse changing process in determining embodied energy flows, which was mainly due to structural factor changes. Moreover, per capita demand continued to boost the net embodied energy outflows of the provinces, with the economic structure relying highly on heavy industries. Finally, we proposed some effective policy suggestions by considering regional heterogeneity. The novelty of this paper is integrating the multi-regional input–output analysis and additive structural decomposition analysis to investigate the trajectory and drivers of China’s energy consumption footprint, respectively. Furthermore, driving factors behind embodied energy transfers were also analyzed.

The socioeconomic factors of investment-induced petroleum consumption: case of fast developing Chinese economy

Capital investment stimulates a sizable portion of petroleum consumption, especially in emerging economies. However, investment-embedded petroleum consumption (IEPC) and the socioeconomic factors that influence it are not well studied. Our study's objective is to close this research gap. Our article estimates the effects of petroleum intensity, technology, investment structure, and economic development on China's IEPC using input–output and bipolar structural decomposition analysis. Additionally, our article develops a previously mostly unknown index of investment intensity. The findings indicated that, on average, between 1990 and 2016, investment induced nearly 30% of China's total final demand-embedded petroleum consumption. On average, petroleum intensity had the most significant decreasing effect on the Chinese IEPC. Averagely, technology had a positive impact, but from 2010 to 2016, it had a noticeable negative impact (− 1.51 exajoule). Both investment intensity and economic development had a significant upward effect. The impact of investment intensity was the smallest of all the factors. Disaggregation of the effects of socio-economic factors at the sectoral level revealed distinct patterns. Thus, by focusing on the socioeconomic dynamics of key sectors, the factors' current decreasing effects can be maximized, and their increasing effects minimized.

Air pollutant emissions induced by rural-to-urban migration during China's urbanization (2005–2015)

As the world's most populous country, China has witnessed rapid urbanization in recent decades, with population migration from rural to urban (RU) regions as the major driving force. Due to the large gap between rural and urban consumption and investment level, large-scale RU migration impacts air pollutant emissions and creates extra uncertainties for air quality improvement. Here, we integrated population migration assessment, an environmentally extended input–output model and structural decomposition analysis to evaluate the NOx, SO2 and primary PM2.5 emissions induced by RU migration during China's urbanization from 2005 to 2015. The results show that RU migration increased air pollutant emissions, while the increases in NOx and SO2 emissions peaked in approximately 2010 at 2.4 Mt and 2.2 Mt, accounting for 9.2% and 8.7% of the national emissions, respectively. The primary PM2.5 emissions induced by RU migration also peaked in approximately 2012 at 0.3 Mt, accounting for 2.8% of the national emissions. The indirect emissions embodied in consumption and investment increased, while household direct emissions decreased. The widening gap between urban and rural investment and consumption exerted a major increasing effect on migration-induced emissions; in contrast, the falling emission intensity contributed the most to the decreasing effect benefitting from end-of-pipe control technology applications as well as improving energy efficiency. The peak of air pollutant emissions induced by RU migration indicates that although urbanization currently creates extra environmental pressure in China, it is possible to reconcile urbanization and air quality improvement in the future with updating urbanization and air pollution control policies.

Understanding wage differences across tourism-characteristic sectors: Insights from an extended input-output analysis

Despite substantial evidence suggesting that final demand is closely related to wages in the tourism industry, little is known about how final demand affects tourism wages. To this end, this study combines input–output analysis and structural decomposition analysis techniques to develop an analytical framework that examines how final demand affects wage changes in tourism-characteristic sectors through industrial linkage mechanisms. The empirical results from China show that the differential contributions of final demand, industrial linkages, and the share of labour returns to sectoral wage changes are the key factors explaining the differences in wage growth across tourism-characteristic sectors in China from 2002 to 2017. For most tourism-characteristic sectors in China, increased consumption and reduced share of labour returns are the main contributors to wage premiums and discounts, but the magnitude of their effects varies across sectors. The production structure effect and import substitution effect present more pronounced sectoral differences in terms of the direction of their effects.

How does China’s industrial structure change affect freight demand?

Freight demand (FD) is closely related to industrial structure (IS), and the determination of this relationship is conducive to the development of the transport industry. This research proposed an input–output structural decomposition analysis (IO-SDA) model to explain the growth of FD with the changes in economic aggregate, structural factor, transport demand intensity, and industry connection. This method enables us to quantify and compare the contribution of industrial development to FD growth of five freight modes and determine the role of the structural factor in the growth of FD. An empirical study was conducted based on China’s 2002 to 2017 input–output tables. The results showed that the construction industry was the most important sector leading to the growth of FD, and the role of the service industry in promoting the growth of FD became more evident. There was a clear trend of concentration to the road freight industry, which may undermine the coordination of the transport industry. The structural factor became an important factor in reducing FD in 2007 to 2017. Based on the findings, it is suggested that China should promote the upgrading of IS and emphasize the importance of green road transport and multimodal transport.

An international comparison analysis of CO2 emissions in the construction industry

AbstractGlobal warming is an increasingly serious issue faced by human beings. As one of the largest contributors to global CO2 emissions, the construction industry plays a significant role in combating global warming and achieving sustainable development. This study aimed to compare the CO2 emissions in the construction industry around the world and analyze the drivers of the differences in construction emission intensities between regions in 2009 using the multiregional input–output model and structural decomposition analysis model, respectively. The results show that China, India, the USA, Russia, and Japan were the largest five construction CO2 emitters. The above five regions, especially China and Russia, had much greater construction emission intensities than the UK, which had the smallest intensity. The findings also demonstrate that the production structure effect was the largest driver enlarging the intensity differences between China and the UK, between India and the UK, between Russia and the UK, and between Japan and the UK, contributing 93.1%, 96.6%, 65.6%, and 85.3% of the differences, respectively. The energy intensity effect dominated the emission intensity difference between the USA and the UK, representing 101.6% of the difference. The top three contributors to the above effects were domestic industries except the production structure effect on the difference between Japan and the UK. This study can help policymakers better understand the disparities of CO2 emissions in the construction industry around the world and thus formulate effective policies to mitigate the emissions.

Decomposition analysis of annual toxicological footprint changes: Application on Japanese industrial sectors, 2001–2015

Abstract The intensive production and use of toxic chemicals have resulted in adverse impacts on human health and the ecosystem. In recent years, global interest and need have called for the examination of the impact of environmental management and sustainable growth strategies on human and ecosystem health. Understanding the drivers of chemical pollution can allow more targeted strategies to be developed to reduce the toxicological footprint (ToxF) of economies. Furthermore, examining temporal trends can evaluate the impact of these drivers on the over-all performance of an economy. In Japan, no comprehensive quantitative research on analyzing the main factors of ToxF has been done previously. To address this gap, this work develops a hybrid approach, which integrates input–output analysis and structural decomposition analysis, to identify the socio-economic determinants of changes in ToxF. This study examines the period between 2001 and 2015 using production and consumption-based perspectives at the sectoral level. The Japanese Pollutant Release and Transfer Register (PRTR) database is used as a reference for the toxic chemical releases while the Eora database is utilized to develop annually competitive input-output tables. The results indicate that the overall ToxF from the Japanese industrial sectors decreased by 49% (−154 kt) during the period analyzed. The consumption structure and emission intensity have significantly contributed to the mitigation of ToxF, corresponding to 317 kt (−101%) and 194.5 kt (−62%), respectively. The production structure and consumption volume were the main driving forces for the increase in ToxF contributing 344 kt (110%) and 12.4 kt (4%), respectively. However, population growth had an insignificant effect on ToxF. The results from this time series analysis of ToxF changes, together with the proposed policy implications, are useful for manufacturers, policy makers and other relevant partners. These insights can be used in developing policies for the management of toxic materials and for establishing less toxic supply chains. Furthermore, insights from this work can be utilized to further develop specific indicators for monitoring the environmental impact of toxic chemicals.

Reassessing pollution haven effect in global value chains

According to the well-known “Pollution Haven Effect”, wealthy countries with strong environmental regulations will have a comparative disadvantage in pollution-intensive industries, and will tend to offshore their polluting industries to poorer countries. We re-test this theory by taking full account of global value chains, which the existing literature has failed to do. Using a multi-country input–output model and structural decomposition analysis, we first calculate the emission intensities using bilateral value-added trade data and isolate the effect of trade composition from the effect of technology. We then investigate the relationship of the trade composition effect with income level for 40 major economies using econometric models. We find that the larger the per capita income gap between importing and exporting countries, the more pollution-intensive the value-added exports. This validates the existence of the Pollution Haven Effect in value-added trade, which is not found when using traditional gross trade data that do not properly account for the global value chains. Our findings suggest that high-income countries offshore their emissions to low-income countries by outsourcing only the dirty production stages instead of the entire production process, and that global value chains are also “global pollution chains.” Based on our novel methodology, our findings explain previous failures in identifying the Pollution Haven Effect and provide a new perspective on global cooperation within environmental governance.

The impact of urbanization and consumption patterns on China's black carbon emissions based on input-output analysis and structural decomposition analysis.

Urbanization in China has dramatically increased from 39.10 in 2002 to 58.52% in 2017. Studies have discussed the impacts of urbanization and its corresponding changes in consumption patterns on carbon dioxide emissions; however, little is known about their impacts on black carbon (BC). Therefore, we collected data on the BC emissions of various sectors to calculate the consumption-based BC emissions in China, and we used an input-output analysis (IOA) and structural decomposition analysis (SDA) to explore the impacts of urbanization and changes in consumption patterns on BC emissions from 2002 to 2017, focusing on sectoral BC emissions. The total BC emissions of various sectors first increased and then decreased. BC emissions increased from 1083.47 in 2002 to 2550.83Gg in 2012. They were then reduced to 2478.63Gg in 2017. Additionally, with the rise in the urbanization rate, household consumption BC emissions increased from 446.18 in 2002 to 1080.12Gg in 2017. Urban consumption, rural consumption, and BC emission intensity were the three main contributing factors to household consumption BC emission changes. Transport, storage, postal, and telecommunications services (TSP); farming, forestry, animal husbandry, and fishery (FFA); and residential and other industries (RES) contributed the most to the urbanization-related BC emission increase. In particular, the TSP sector contributed the most to the BC emission increase because of the increasing TSP needs related to urbanization. Therefore, it is necessary to formulate mitigation policies for the TSP sector.

Framework for accounting for tourism carbon emissions in China: An industrial linkage perspective

Understanding tourism carbon emissions and their influencing factors from the perspective of industrial linkages can inform policy-making in the development of sustainable tourism. Based on a combination of the environmental input–output (I-O) model and structural decomposition analysis, this article develops a novel framework for analyzing the industrial linkage pathways of China’s carbon emissions linked to tourism and identifying the driving factors affecting change in carbon emissions embodied in the supply chain. Results reveal that most carbon emissions linked to China’s broad-sense or narrow-sense tourism industry derive from some critical upstream industries, that is, indirect carbon emissions resulting from the intermediate production processes. Significant differences exist in the industrial linkage pathways of carbon emissions between tourism subsectors; thus, emission reduction policies for the broad-sense or narrow-sense tourism industry should be formulated based on these key interindustrial linkage pathways. The direct energy consumption intensity effect and energy structure effect are beneficial to carbon emission reduction, while the I-O structure effect reverses the effect on carbon emission reduction from negative to positive.

Revealing environmental inequalities embedded within regional trades

Carbon emissions embodied in interprovincial trade (CEE-IT) are closely related with the environmental responsibility allocation. Besides the perspective of administrative division, more rational and effective clusters based on provincial characteristics is more conducive for understanding the regional emission reduction linkages and simplifying the steps of responsibility determination. To provide a reasonable management of CEs transference mitigation in China, this study develops a provincial clustering scale CEE-IT model through three-scale accountings (i.e., aggregated-scale, consumption-scale and income-scale analysis). Specifically, 30 provinces are aggregated into several new regions with similar characteristics. Carbon emissions from different energy sources are first considered to distinguish and specify different emission reduction modes. The input output analysis (IOA) and structural decomposition analysis (SDA) are applied to quantify the embodied interprovincial carbon emissions and the relative contributions of socio-economic factors at the sector-level of disaggregation and aggregation, respectively. Three-scale accountings are innovatively employed into EEBT model to deeply analyze the emissions along China’s domestic inter-regional supply chains for identifying regional production, consumption and income-based emission responsibilities. Based on three accounting perspectives, the results provide suggestions for coordinated emission reduction across regions (including specific provinces) from the overall and decomposition levels. It shows that cutting the imports of mining sector for all regions could reduce emissions from the supply side. Rural household consumption and fixed capital formation are the major drivers for Ⅳ region from the consumption side. Technological innovations in Ⅲ region have reduced carbon emissions by 55.2% and contributed 159 Mt reductions from 2007 to 2012. Ⅲ region is insensitive to energy types and the utilization of crude oil in Ⅰ region limit the improvement of its system efficiency. Importing large quantities of emission-intensive products from Beijing and Jiangsu is a cause of high income-based emissions in Ⅱ region.

Why Are the Carbon Footprints of China’s Urban Households Rising? An Input–Output Analysis and Structural Decomposition Analysis

A rise in China’s economy and urbanization has brought about obvious growth in the carbon footprints of urban households over the past years. In this study, input–output analysis was adopted to calculate the carbon footprints of urban households in China. Furthermore, a detailed analysis of the impact factors on indirect carbon footprints was carried out by using the structural decomposition analysis at both holistic and sectoral levels. The results showed that the carbon footprints of urban households were 941.37 MtCO2 in 2002, 1498.11 MtCO2 in 2007, and 2139.50 MtCO2 in 2012. Electricity was the main contributor to the direct urban household carbon footprints, accounting for over 40%. The category of “household facilities” contributed the most to indirect carbon footprints (306.00 MtCO2) in 2012, and the “transportation” had the fastest growth rate (395%) during 2002–2012. The industrial sector of “wearing apparel” had the largest increments (139.92 MtCO2) in carbon footprints during the past decade. Generally, consumption level and population size presented positive effects on the increments in indirect carbon footprints, while emission intensity decreased indirect carbon footprints. However, the impact of consumption structure and intermediate demand on indirect carbon footprints varied at holistic and sectoral levels. The mitigation efforts should focus on reducing emission intensity, shifting consumption structure and changing intermediate demand.

Gains or Pains? Effects of US–China Trade on US Employment: Based on a WIOT Analysis from 2000 to 2014

ABSTRACT This article examines the role of US–China trade in the changes of US employment. Through an input–output framework and structural decomposition analysis method, we decompose US employment and show the importance of all the determinants of employment in one place. The result shows that, over the period of 2000–2014, improvement in labor productivity was the dominant cause of US job losses, whereas only approximately 2% of the job decline was due to imports from China. The negative impact of imports from China for consumption demand is greater than for investment demand because imports from China for consumption demand have higher labor intensity. US employment depends on intermediate exports more than on final exports, and imports from China embody more services as intermediate inputs and, therefore, contribute indirectly to the employment of services in the US.

Main Drivers of Carbon Dioxide Emissions in Vietnam Trajectory 2000-2011: An Input-Output Structural Decomposition Analysis

Vietnam’s rapid economic growth has resulted in serious environmental concerns both at local and global scales. In-depth understanding of the key factors behind the rapid growth of CO2 emissions is of great significance in the development of local and global climate policies. Furthermore, this provides insight into how emerging economies can develop a low emission future. Recent works have demonstrated the effectiveness of the input–output model and structural decomposition analysis in analyzing how changes in different socio-economic factors affect energy-based CO2 emissions in the sectoral level using production and consumption-based perspectives. In the context of Vietnam’s economy, such aspects have not been fully explored in previous literature. This study thus analyzes the driving forces responsible for the increase in CO2 emissions in Vietnam from both production and consumption perspective during periods 2000 – 2007 and 2007 - 2011. The results using the production perspective indicate that during 2000 - 2011 the incremental change in CO2 emissions in Vietnam is driven mainly by the consumption structure (100.5%) and consumption volume (219.4%) which are offset by the decline in technology (-132.7%) and production structure (-22.5%). Population (24.1%) had a small effect on total CO2 emissions. Results using the consumption perspective show that even with large variations between the two periods, household, export and investment are the main drivers responsible for the sharp increase in CO2 emissions. This is offset by the decrease in import factor. Policy implications indicate that improving technology, adjusting production and consumption structure, and optimizing international trade are important factors for alleviating CO2 emissions in Vietnam.

CO2 emissions from the industrialization and urbanization processes in the manufacturing center Tianjin in China

Curbing the rapid CO2 emissions and reaching the emission peak before 2030 under socioeconomic transition is a great challenge facing China. Therefore, in–depth understanding of the impact of industrialization and urbanization processes on CO2 emissions is essential for the achievement of China's CO2 emission reduction targets. In this study, we apply an analysis framework based on input–output analysis and structural decomposition analysis to analyze the CO2 emissions cost of the industrialization and urbanization processes from 1997 to 2012, using the manufacturing center Tianjin as an example. The CO2 emissions of Tianjin tripled during the 15-year studied period. The industrialization process strongly increased CO2 emissions through the rapid growth of the final demand level, while energy intensity improvement played an important role in curbing the rapid emission growth from 1997 to 2012. With respect to specific industries, with a growing share in economic structure, construction was the largest source in the overall CO2 emissions structure. As the pillar industry, transport equipment and information technology (IT) machinery industry were the largest sources in net export-related CO2 emissions, and the IT machinery industry also contributed the most to CO2 emissions growth related to production structure changes. Growing importance of tertiary industry, services (including producer services and consumer services) led to CO2 emissions growth through the growing share in net export and final demand structure during the 2007–2012 period. Besides the influence of industrialization, the rapid urbanization process resulted in a 74.1% growth in household consumption-related CO2 emissions, and the gaps between rural and urban continued to increase. The high demand for city infrastructure and building construction originated from investment also caused a great CO2 emission growth (142.5 Mt, 86.6% CO2 emission growth in Tianjin caused by investment). These results also provide policy implications for curbing the CO2 emissions increase in Tianjin as well as in other cities.