Stochastic Impacts By Regression On Population, Affluence, And Technology Research Articles

Impact of Urbanization on Carbon Dioxide Emissions—Evidence from 136 Countries and Regions

Urbanization affects economic production activities and energy demand, as well as lifestyle and consumption behavior, affecting carbon dioxide emissions. This study constructs the System Generalized Method of Moments (Sys-GMM) model of the impact of urbanization rate on carbon dioxide emissions based on panel data of 136 countries and regions in the world from 1990 to 2020, grounded on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. This study found that (1) there is a negative relationship between urbanization rate and CO2 emissions from 1990 to 2020. (2) The impact of the urbanization rate on CO2 emissions is heterogeneous. An increase in urbanization rate in non-OECD countries significantly reduces CO2 emissions, while the effect is not significant in OECD countries. (3) The carbon intensity of fossil energy consumption moderates the relationship between urbanization rate and CO2 emissions, weakening the effect of urbanization rate on CO2 emissions. Based on these findings, policy recommendations such as promoting urbanization and increasing the regulation and control of fossil energy carbon intensity are proposed.

Decomposition and scenario analysis of agricultural carbon emissions in Heilongjiang, China.

As a key agricultural region in China, Heilongjiang Province has experienced significant carbon emissions over the past few decades. To understand the underlying factors and future trends in these emissions, a comprehensive analysis was conducted from 1993 to 2030. The agricultural carbon emissions from 1993 to 2020 were estimated using the emission factor method. To analyze the influencing factors and future trends of these emissions, the study employed the Logarithmic Mean Divisia Index (LMDI) and integrated it with the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. Results showed that (1) the agricultural carbon emissions in Heilongjiang were primarily driven by rice cultivation, followed by fertilizer production and irrigation electricity. (2) The economic and labor structure effects were the main driving factors of agricultural carbon emissions, while the population, demographic, and intensity effects were the main inhibitors. (3) Agricultural carbon emissions in Heilongjiang Province peaked in 2016 with 69.6 Mt CO2-eq and could subsequently decline by -3.92% to -4.52% between 2020 and 2030 in different scenario simulations. In the future, Heilongjiang Province should prioritize the reduction of agricultural carbon emissions from rice production. Adjusting the planting structure, managing the layout of rice paddies, and promoting the cultivation of dry rice varieties would significantly contribute to mitigating agricultural carbon emissions.

Sectoral carbon dioxide emissions and environmental sustainability in Pakistan

Global greenhouse gases and carbon dioxide emissions have escalated to concerning levels. Given the growing urbanization, industrialization, and energy consumption, it is crucial to understand how carbon dioxide emissions from various sectors influence environmental sustainability in Pakistan. The prime objective of this study is to examine the nexus between sectoral carbon dioxide emissions and environmental sustainability in Pakistan, analyzing data from 1971 to 2014. The study employs the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) method and the Autoregressive Distributive Lag (ARDL) model to analyze patterns and relationships, providing insights into how each sector's emissions contribute to the overall environmental impact. The results highlight that the independent variables – economic growth, population growth, and energy consumption - are the most significant contributors to overall carbon dioxide emissions, driven by the high consumption of fossil fuels. At an aggregate/disaggregate level, various models show mixed associations between dependent variables such as overall carbon dioxide emissions, carbon dioxide emissions from gaseous fuel consumption, carbon dioxide emissions from liquid fuel consumption, carbon dioxide emissions from solid fuel consumption, carbon dioxide emissions from residential buildings, commercial and public services, and carbon dioxide emissions from the transportation sector with the independent variables. Pairwise Granger causality confirms a unidirectional causality among various pairs of relationships. The study suggests that policymakers in Pakistan adopt a multi-sectoral approach to achieve environmental sustainability. It also recommends accelerating the transition to renewable energy sources such as solar, wind, and hydropower to reduce dependence on fossil fuels.

The forecast and low‐carbon performance of land use in rapid urbanization area under the low‐carbon oriented spatial planning: Evidence from Hangzhou, China

AbstractThe introduction of the carbon peak and carbon‐neutral targets by many countries' central governments has put low‐carbon‐oriented spatial planning at the forefront of discussions. However, few studies have focused on the balance of carbon emission reduction and economic goals in spatial planning, and the governance influence on land use change simulation. This study addresses this gap by conducting an empirical analysis in the rapidly urbanizing area of Hangzhou, China, taking into consideration low‐carbon constraints and economic development demands. Using the stochastic impacts by regression on population, affluence, and technology (STRIPAT) model and linear programming–Markov, we simulate the governance decision‐making process to calculate the optimal land‐use structures under both low‐carbon and baseline scenario, then simulated land use patterns by using artificial‐neural‐network‐based cellular automata (ANN‐CA). The results showed 12.35% and 2.5% growth in urban and forest land, and 9.69% and 6.4% decline in farm and rural land under the low‐carbon scenario. 92.31% of urban land change occur in the downtown districts and suburbs; while 59.77% of farm land change and 95.53% of forest land change occur in the exurban districts. The low‐carbon performance of land use was reflected in carbon storage release, carbon emission capability change, and low‐carbon capability. The most common conversion of land use categories under the low‐carbon scenario was between farm and forest land, and between rural and urban land, which resulted in less carbon storage release and carbon emissions compared with the baseline scenario. Furthermore, under the low‐carbon scenario, the compactness of construction land increased by 2 × 10−5, while its fragmentation decreased by 0.0027. This study sheds light on the impact of low‐carbon‐oriented land use planning on urban land expansion, providing empirical evidence for city governments in rapid urbanization areas to improve land use efficiency.

The impact of TOURISM and seasonality on different types of municipal solid waste (MSW) generation: The case of Ibiza

Most studies analysing waste generation consider the impact on the resident population; however, in the case of tourist destinations, it is important to differentiate the impact between the local population and tourists. This research separates these two population groups using the stochastic impacts by regression on population, affluence and technology (STIRPAT) model for the case of Ibiza. The results confirm different waste generation patterns in tourist and non-tourist populations depending on the waste type (non-sorted and sorted). Moreover, our analysis found that the amount of waste generated per person decreased over time, demonstrating the growth of circular economy practices in the destination. Furthermore, the STIRPAT model highlights the development of a circular economy, showing a downward trend in the generation of non-sorted waste and a positive trend towards collecting recyclable materials. Finally, the results confirm that waste generation patterns vary by season, population, and waste type. Additionally, for both populations, there is a reduction in per capita non-sorted waste generation (this is more significant for the non-tourist population). In contrast, per capita sorted waste generation has increased for both types of population.

Comprehensive assessment of refined greenhouse gas emissions from China's livestock sector

Livestock and poultry products are an essential human food source. However, the rapid development of the livestock sector (LS) has caused it to become a significant source of greenhouse gas (GHG) emissions. Consequently, investigating the spatio-temporal characteristics and evolution of GHG emissions is crucial to facilitate the green development of the LS and achieve “peak carbon and carbon neutrality”. This study combined life cycle assessment (LCA) with the IPCC Tier II method to construct a novel GHG emissions inventory. The GHG emissions of 31 provinces in China from 2000 to 2021 were calculated, and their spatio-temporal characteristics were revealed. Then, the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model was used to identify the main driving factors of GHG emissions in six regions of China and explore the emission reduction potential. The results showed that GHG emissions increased and then decreased from 2000 to 2021, following a gradual and steady trend. The peak of 628.55 Mt CO2-eq was reached in 2006. The main GHG-producing segments were enteric fermentation, slaughtering and processing, and manure management, accounting for 45.39 %, 26.34 %, and 23.08 % of total GHG emissions, respectively. Overall, the center of gravity of GHG emissions in China migrated northward, with spatial aggregation observed since 2016. The high emission intensity regions were mainly located west of the “Hu Huanyong line”. Economic efficiency and emissions intensity were the main drivers of GHG emissions. Under the baseline scenario, GHG emissions are not projected to peak until 2050. Therefore, urgent action is needed to promote the low-carbon green development of the LS in China. The results can serve as scientific references for the macro-prevention and control of GHG emissions, aiding strategic decision-making. Additionally, they can provide new ideas for GHG accounting in China and other countries around the world.

Historical development, impact mechanism and future trends of nitrogen footprint in Wuxi City, China

Human activities have changed the biogeochemical cycle of nitrogen, leading to a large amount of reactive nitrogen (Nr) into the environment, aggravating a series of environmental problems, affecting human and ecosystem health. Cities are the core areas driving nitrogen cycling in terrestrial ecosystems, however, there are numerous influencing factors and their contributions are unclear. The nitrogen footprint is an important index to understand the impact of human activities on the environment, however, the calculation of urban nitrogen footprint needs a simplified and accurate system method. Here we use a nitrogen footprint calculation model at the urban system level based on system nitrogen balance, and a multi-factor extended STIRPAT (stochastic impact by regression on population, affluence, and technology) model suitable for analyzing the impact mechanism of nitrogen footprint to estimate nitrogen footprint of Wuxi City during 1990–2050. We find that: (1) from 1990 to 2020, the total nitrogen footprint of Wuxi City was in an increasing trend, but the per capita nitrogen footprint was in a decreasing trend. The per capita nitrogen footprint of 22.36 kg capita−1 in 2020 was at a lower level globally. (2) Nr discharge from fossil fuel combustion and Haber-Bosch nitrogen fixation accounted for the main proportion of nitrogen footprint. (3) Dietary choice (Ad), GDP per capita (Ag), urbanization rate (Au), population (P), and fossil energy productivity (Te) were the key factors contributing to the increase of the nitrogen footprint, which resulted in an annual increase of 1.39 %. While nitrogen footprint productivity (Tn), nitrogen use efficiency in crop farming (Tc), and nitrogen use efficiency in animal breeding (Ta) were the key inhibit factors that inhibit the increase of nitrogen footprint, and these factors slow down the annual growth rate of nitrogen footprint by 0.39 %. (4) The continuous growth of nitrogen footprint in the baseline and population growth scenarios will bring more environmental problems and greater environmental governance pressure to Wuxi City, while the sustainable scenario that includes comprehensive means such as economic adaptation and technological improvement is more in line with the requirements of high-quality development in China. Several mitigation measures are then proposed by considering Wuxi's realities from both key impact factors and potential for nitrogen footprint reduction in different scenarios, which can provide valuable policy insights to other cities, especially lakeside cities to mitigate nitrogen footprint.

Decomposition of China’s Carbon Emissions Responsibility from the Perspective of Technological Heterogeneity

A global agreement has been reached on the reduction in greenhouse gas emissions. Worldwide, countries have implemented measures to tackle carbon emission issues by establishing aggregate targets and decomposing responsibilities. This study aims to decompose carbon emissions by creating an input–output model that incorporates multivariate factors like energy consumption and water consumption, together with a ZSG-DEA (zero-sum data envelopment analysis) model considering technological heterogeneity (Tech-ZSG-DEA). Based on the total carbon emission data predicted using the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the carbon emission efficiency of 30 provinces in China is evaluated according to multiple principles. This is achieved by considering variations in population, economy, energy consumption, and water consumption across different locations. The efficiency findings indicate a discrepancy between the initial allocation and the highest efficiency value of 1. The traditional ZSG-DEA model overlooks regional disparities and may worsen carbon emission pressures in less developed areas. In contrast, the Tech-ZSG-DEA model, which considers regional technological diversity, allows more efficient regions to help alleviate some of the carbon emission burden and considers economic and social benefits. There is a large difference in the emission responsibility of the provinces based on the different decomposition principles. Finally, relevant policy recommendations are provided, such as the formulation of differentiated and inclusively coordinated emission plans. In addition, there are also mechanisms for coordinating interests and joint prevention among different regions.

An Empirical Investigation of GDP, Industrialization, Population, Renewable Energy and CO2 Emission in Bangladesh: Bridging EKC-STIRPAT Models

This research paper explores the presence of the Environmental Kuznets Curve (EKC) in Bangladesh, delving into the intricate relationships among GDP, industrialization, renewable energy utilization, and CO2 emissions. Utilizing the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, our investigation spans the 1992 to 2021 timeframe, offering a comprehensive analysis of the interplay between these crucial factors in the context of Bangladesh's environmental sustainability. In this study, the F-bound test is utilized to establish the cointegration relationship among variables. And the short-run and long-run elasticity of explanatory variables is investigated by employing the Autoregressive Distributed Lag (ARDL) approach. This research also employed a pairwise granger causality test to explore the direction of causality between them. Empirical results prove the existence of a cointegration relationship among CO2 emission, GDP per capita, a quadratic form of GDP per capita, renewable energy, industrialization, and population. The findings provide strong support for the presence of the EKC in the case of Bangladesh. Besides that, significant inverse relationship was also obtained between CO2 emission and renewable energy consumption. Additionally, the study found the detrimental long-term effects of industrialization on the environment. The EKC hypothesis argues that Bangladesh can accomplish sustainable economic development. To attain the goal country should adopt appropriate government policies and ensure their implication. This study strongly advocates using sustainable energy sources and implementing regulations on pollutant industries.

Low-carbon planning of urban charging stations considering carbon emission evolution characteristics and dynamic demand

As a new generation of transportation, electric vehicles play an important role in carbon-peak targets. The development of electric vehicles needs the support of a charging network, and improper planning of charging stations will result in a waste of resources. In order to expand the charging network of electric vehicles and give full play to the low-carbon and efficient characteristics of electric vehicles, this paper proposed a charging station planning method that considers the characteristics of carbon emission trends. This paper combined the long short-term memory (LSTM) network with the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model to predict the carbon emission trend and quantified the correlation between the construction speed of a charging station and the evolution characteristics of carbon emission by Pearson’s correlation coefficient. A multi-stage charging station planning model was established, which captures the dynamic characteristics of the charging demand of the transportation network and determines the station deployment scheme with economic and low-carbon benefits on the spatiotemporal scale. The Pareto frontier was solved by using the elitist non-dominated sorting genetic algorithm. The model and solution algorithm were verified by the actual road network in a certain area of Shanghai. The results showed that the proposed scheme can meet the charging demand of regional electric vehicles in the future, improve the utilization rate of charging facilities, and reduce the carbon emission of transportation networks.

Reconsidering the Long-Term Impacts of Digitalization, Industrialization, and Financial Development on Environmental Sustainability in GCC Countries

Gulf Cooperation Council (GCC) countries have faced environmental challenges in recent decades. This study aims to identify the contribution of digitalization, industrialization, and financial development to the ecological footprint (EF) in GCC countries between 2000 and 2021. The empirical investigation involves estimating the STochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model using the augmented mean group (AMG), common correlated effects mean group (CCEMG) and cross-sectionally augmented autoregressive distributed lag (CS-ARDL) estimators. The findings reveal the existence of long-term linkages between EF and the factors mentioned above. Furthermore, there is evidence that adopting digitalization and information and communication technologies (ICT) improves long-term environmental quality. In contrast, both industrialization and financial development exert detrimental effects on the environment. Finally, the JKS Granger non-causality test revealed that all variables, except financial development, predict environmental degradation in GCC countries. These findings can assist in formulating efficient strategies to reduce ecological degradation and achieve environmental sustainability in GCC countries.

The impact of urbanization and economic growth on carbon dioxide emission in sub-Saharan African countries: a perspective from the spatial-temporal approach.

Sub-Saharan Africa (SSA) is seeing exceptional urbanization and economic expansion rates. Therefore, the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) parameters and the spatial econometric framework are used in this work to examine the influence of economic growth and urbanization on SSA's CO2 emissions. Likewise, to determine the spatial effect and understand how factors influence the spatial dependence of carbon emissions, the study builds a spatial Durbin model (SDM). In line with the findings, the spatial correlation test revealed the spatial correlations across various countries. This indicates that the changes in sub-Saharan African country's CO2 emissions impacted nearby countries and the countries themselves. Additionally, the findings reveal that, in the SSA's countries, urbanization, economic growth, industrial structure, trade, and population, excluding energy intensity, which failed the significant test, all positively influence CO2 outflows, in line with the spatial econometric model's findings. Thus, energy intensity shares an adverse impact on carbon emissions. As an outcome, energy intensity reduces carbon dioxide emissions in nearby nations and the entire region. Thus, the study recommends that policymakers account for the effects of spatial spillover when establishing low-carbon policies, encouraging a low-carbon lifestyle, promoting environmentally friendly technologies, and improving regional collaboration.

Pollution load estimation and influencing factor analysis in the Tuhai River Basin in Shandong Province of China based on improved output coefficient method.

Estimating the pollution loads in the Tuhai River is essential for developing a water quality standard scheme. This study utilized the improved output coefficient method to estimate the total pollution loads in the river basin while analyzing the influencing factors based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Findings indicated that the projected point source pollution loads for total phosphorus (TP), chemical oxygen demand (COD), and ammonia nitrogen (AN) would amount to 3937.22 ton, 335,523.25 ton, and 13,946.92 ton in 2021, respectively. Among these, COD pollution would pose the greatest concern. The primary contributors to the pollution loads were rural scattered life, large-scale livestock and poultry breeding, and surface runoff. Per capita GDP emerged as the most influential factor affecting the pollution loads, followed by cultivated land area, while the urbanization rate demonstrated the least impact.

Analyzing the relationship between energy use for economic development and CO2 emissions, crop, and livestock production in Pakistan by using the extended STIRPAT model

Contemporary human-made activities are responsible for the emission of more than 30 billion tons of carbon dioxide (CO2), a greenhouse gas, into the atmosphere. The current study's primary purpose is to examine the key elements that contribute to the elevated levels of the release of CO2 into the environment in Pakistan. The research used Pakistan's annual data spanning from 1970 to 2020, along with the STIRPAT (Stochastic Impact by Regression on Population, Affluence, and Technology) model. The relationship between the emission of CO2 into the atmosphere and other chosen factors is examined using the ARDL (Auto Regressive Distributive Lag) model and the ECM (Error Correction Model). These models help to establish the credibility of the acquired findings. The paired Granger causality analysis revealed the presence of unidirectional and bidirectional cause-and-effect relationships between the named variable parameters in the activity under investigation. Pakistan must prioritize tackling the fundamental challenges afflicting its farming industry, particularly those pertaining to the efficiency of its livestock and crop production. The novelty of this study comes in its investigation of the interaction between hitherto undiscovered macro-level properties and the emission of CO2 into the environment of Pakistan. The findings may assist policymakers in formulating an environmental and agricultural strategy aimed at promoting the use of sophisticated low-carbon technology.

Institutions and carbon emissions: an investigation employing STIRPAT and machine learning methods

We employ an extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model combined with the environmental Kuznets curve and machine learning algorithms, including ridge and lasso regression, to investigate the impact of institutions on carbon emissions in a sample of 22 European Union countries over 2002 to 2020. Splitting the sample into two: those with weak and strong institutions, we find that the results differ between the two groups. Our results suggest that changes in institutional quality have a limited impact on carbon emissions. Government effectiveness leads to an increase in emissions in the European Union countries with stronger institutions, whereas voice and accountability lead to a fall in emissions. In the group with weaker institutions, political stability and the control of corruption reduce carbon emissions. Our findings indicate that variables such as population density, urbanization and energy consumption are more important determinants of carbon emissions in the European Union compared to institutional governance. The results suggest the need for coordinated and consistent policies that are aligned with climate targets for the European Union as a whole.

Evaluating the asymmetric effect of patents driven environmental technologies on environmental degradation in the E7 countries: An extended model of STIRPAT

AbstractThis research employed an extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) modelling providing original empirical evidence on how technological innovation with the presence of green finance, energy transition vectoring renewable and non‐renewable energy, population, affluence, and financial development affect environmental sustainability measured on the carbon emissions and ecological consequences in E7 during the 1996–2019 period. The study applied mean group (MG), pooled mean group (PMG), and dynamic fixed effects (DFE) after conducting various preliminary tests. The findings predominantly revealed that renewable energy, green finance, and the patent in environmental technologies play an important role in mitigating environmental degradation. However, non‐renewable energy, financial development, population growth, and affluence escalate environmental degradation in the emerging countries under investigation. The outcome of this research proved conclusively that it is vital to introduce the patent on environmental technologies to encourage the collaboration on technology development related to global climate change. Furthermore, enhancing international collaboration in research and technology will positively affect local businesses through utilizing the modern climate change mitigation.

Predicting combined carbon emissions in urban regions considering micro-level enterprise electricity consumption data and macro-level regional data

In the context of “dual carbon” goals, governments need accurate carbon accounting results as a basis for formulating corresponding emission reduction policies. Therefore, this study proposes a combined carbon emission prediction method for urban regions, considering micro-level enterprise electricity consumption data and macro-level regional data. Considering the different applicability of prediction methods and the requirements for the data volume, a region-level carbon emission prediction method based on the long short-term memory neural network is proposed, which takes into account the micro-level electricity–carbon coupling relationship. Additionally, a region-level carbon emission prediction method based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) is proposed, considering the macro-level economic–carbon coupling relationship. The generalized induced ordered weighted averaging method is employed to assign differential weights to micro- and macro-prediction values, yielding regional carbon emission predictions. An empirical analysis is conducted using a key city in the eastern region as an example, analyzing the main influencing factors and predicting carbon emissions based on relevant data from 2017 to 2021, and the accuracy of the models is analyzed and validated.

Four-decades evolutionary development of municipal solid waste management in China: Implications for sustainable waste management and circular economy.

This study collected data on waste generation and management in China between 1979 and 2020 from government statistics and literature and reviewed the development of municipal solid waste (MSW) management in China. The extended stochastic impact by regression on population, affluence and technology (STIRPAT) model was employed to identify the driving forces of MSW generation, and the cointegration analysis showed that economy (0.35, t = -3.47), industrial structure (3.34, t = -20.77) and urbanization (-1.5, t = 5.678) were the significant socioeconomic driving forces in the long run. By employing the framework of evolutionary economics, this study then investigated the internal rules of long-term interaction between socioeconomic factors and MSW management. The results indicate that, in the long run, MSW management development can be viewed as an evolutionary process that includes a continuous adaptation to external socioeconomic factors and the co-evolution of internal institutions and technologies. Adaptation and diversity of institutions and technologies play an important role in achieving sustainable waste management and circular economy (CE). This study offers a novel evolutionary perspective for explaining dynamic changes of MSW management in China, as well as recommendations for emerging economies to achieve sustainable waste management and CE goals.

Analysis of influencing factors of carbon emissions in China based on the STIRPAT model

China, as a major economic power, has been increasing its carbon emissions year after year. Effectively controlling carbon emissions and finding suitable and effective methods to reduce emissions have become the main research themes of current research. The Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model is used in this work to analyze the impact of GDP, population, urbanization, and energy intensity on Chinas carbon emissions from 2003 to 2020. From the output by the SPSS software, it can be illustrated that GDP and energy intensity have more obvious contribution on carbon emission, while urbanization level and population dont. Additionally, as the GDP index increases by a value of one, a 1.220 change will be seen by the carbon emission. Similarly, every one unit change for energy intensity is associated with 0.897 change in carbon emission. Therefore, this paper can consider effective ways to conserve energy and mitigate greenhouse gas emissions from these two aspects, and in this way attain the objective of carbon peaking and carbon neutrality.

Inversed virtual water flow pattern and its influencing factors in Northwest China

Northwest China faces an imbalance between scarce water resources and increasing water use demands for agricultural production and economic growth. However, the shifts in virtual water flow (VWF) patterns in Northwest China and its the driving factors are still unclear. In this study, we employed the Multiregional Input–Output (MRIO) method to investigate the evolution of VWF patterns among Northwest China’s primary, secondary, and tertiary industries and analyze their driving factors through Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. The results show that (1) Northwest China changed from 7.24 × 108 m3 net inflow to 175.70 × 108 m3 net outflow from 2002 to 2007. (2) More than 90 % of virtual water of the primary industry was used locally, but both secondary and tertiary industries depended more on virtual water inflow in Northwest China. (3) The virtual water outflow from Northwest China is mostly to the Jing-Jin Region, followed by the Northern Coast, and the main inflow is from the Central Region. (4) Population, economy, and technology affect virtual water from inflow to outflow, and technological development helps to decrease virtual water flow volumes. In conclusion, dramatic decrease of second and third industries inflow is the main reason for the changes from net inflow to net outflow of Northwest China. Meanwhile, it is feasible to increase technological support to further reducing virtual water consumption in primary industry, and strengthen virtual water trades of secondary and tertiary industrial virtual water inflows by promoting population and economic conditions.