Search

This paper investigates the nature of the associations and the potential existence of both short-run and long-run relationships between the stock market indices of Morocco, France, Germany, the United Kingdom, China, and the United States from January 2014 to January 2024. The purpose of analyzing dynamic interconnections and contagion effects is to determine how the stock markets of these countries influence and relate to each other. The study employs a time series Vector Error Correction Model (VECM) approach, incorporating stationarity, cointegration, and Granger causality tests. Additionally, the Impulse Response Function (IRF) is used to analyze the response of variables to shocks. The bivariate Granger causality test reveals significant causal influences: from France, Germany, and the USA to Morocco; from the USA to the DAX and France; and from the UK to Germany. After establishing the Granger causal relationships, long-run and short-run relationships are further examined. Using the Johansen multivariate cointegration approach, the study suggests a long-term equilibrium among the six stock market indices over time. The short-run adjustments are analyzed using the VECM, which reveals that adjustments in the CAC 40, DAX, and MASI tend to correct deviations from equilibrium, indicating a tendency to move towards equilibrium. For the FTSE 100, S&P 500, and SSEC, the VECM captures the speed and direction of adjustments as these indices respond to short-term disruptions and work towards restoring equilibrium. The findings underscore the importance of closely connected global stock markets, which means that international regulators must coordinate their efforts to reduce the risks of contagion. Policymakers should prioritize improving financial stability through integrated frameworks considering short-term disruptions and long-term equilibrium trends. [ABSTRACT FROM AUTHOR]

This research was conducted to determine the short-term and long-term effects of inflation, exchange rates, interest rates, and the money supply on economic growth in Indonesia from 2009 to 2022 using the Vector Error Correction Model (VECM) method. The VECM method is used to analyze the interaction between these variables over different time horizons, offering valuable insights into their respective roles in influencing economic growth. The results show that in the short run, the exchange rate variable does not have a significant effect on the economic growth, while in the long run the interest rate variable has a positive impact on the economy with a negative coefficient value. The short run variable interest rates do not have significant effects on the growth of the economy, but in the longer run interest rates have an important effect on growth. In conclusion, the effect of exchange rates on the Indonesian economy is still a controversial research topic. The findings enhance the current literature on macroeconomic policy and provide a foundation for policymakers to design more effective economic strategies, especially in addressing the challenges posed by inflation and exchange rate volatility in both the short and long terms. [ABSTRACT FROM AUTHOR]

Purpose This study examined the effect of the risk-free rate of return and other macroeconomic variables on the stock prices of firms listed on the Pakistan Stock Exchange-100 (PSX). Methodology The data from 2013 to 2023 was collected from the Karachi Stock Exchange website, Yahoo Finance, and the State Bank of Pakistan’s website. The main techniques for data analysis were Johansen-Juselius (J.J.) cointegration and the Vector Error Correction Model (VECM). Findings The findings show that a risk-free rate significantly impacts stock returns in the long run. Further, foreign direct investment (FDI) has a significant positive impact on the stock return of listed companies. The cointegration result indicates a long-run association among all the selected variables. Moreover, the results of the VECM show that the error correction term (ECT) in VECM (-1) is negative (-0.150) and significant. The negative coefficient of 0.150 indicates that deviations from the long-run equilibrium will be corrected at a speed of 15% per period, approximately within 7 months. Conclusion The study concludes that stock prices and macroeconomic indicators have long-run associations. Conversely, changes in the risk-free rate of return by the government or the banking sector have the opposite effect on stock prices. This study assists government officials, stock market participants, and policymakers in determining the profitability of the National Savings Scheme and Banks.

The goal of this article is to analyze the impact of household savings and household loans on gross fixed capital formation in Bulgaria. The research is carried out on the basis of quarterly data for the 2010-2023 period. The sources of the data are the National Statistical Institute (NSI) and the Bulgarian National Bank (BNB). The algorithm of the empirical research includes: checking for stationarity by means of the extended Dickey-Fuller test; determining the optimal lag of the model; Johansen cointegration test; specification of vector error correction model (VECM). As a result of the research, it has been established that there is a long-term relationship between the variables gross fixed capital formation, household savings and household loans. At the same time, about 39.2% of the disequilibrium between the studied variables in a certain quarter is compensated in the next one. [ABSTRACT FROM AUTHOR]

This research discusses the impact of climate change on inflation, as well as the limitations of existing econometric models in incorporating multiple climate-related variables in determining inflation. To address this limitation, the study introduces the climate condition index (CCI) for 153 countries covering the 1901–2021 period to investigate the relationship between climate and agricultural inflation. This index represents a weighted average of various climate variables, including cloud coverage, temperature, precipitation, vapor pressure, and wet day frequency. The weights are the long-term cointegration coefficients of the climate variables concerning the Agricultural Producer Price Index (APPI), which were obtained from separate vector error correction models (VECMs) constructed for each country. The study shows that climate change has a significant impact on agricultural prices globally, as evidenced by a median Granger causality p-value of 0.036 across 153 countries (CCI Granger causing APPI in 127 countries with a 90% confidence interval). The results align with panel vector autoregression and country-specific VECMs impulse responses, which indicate that climate conditions negatively impact agriculture in 142 countries, while 11 countries gain from climate change. The study's overall results suggest that climate change substantially affects agriculture globally. In light of the significant economic reliance on agricultural production, it is advisable for governments to consider the issue from the perspective of sustainable growth, while central banks should tackle it with a focus on the inflationary implications through green quantitative easing. [ABSTRACT FROM AUTHOR]

This study evaluates the trend and growth pattern of international tourism and analyzes the impact of tourism on the economic growth of Kerala for the past four decades from 1980 to 2019. The time series analysis employed in this study uses the secondary data on Net State Domestic Product (NSDP) of Kerala at constant prices, foreign tourist arrivals (FTA), and foreign exchange earnings (FEE) at constant prices, collected from various sources of the State and Central Government. The methodology of this study uses the Augmented Dickey Fuller (ADF) test for unit root, followed by the Johansen Cointegration test, the Vector Error Correction Model (VECM), and the Granger Causality test. The results of the analysis reveal the existence of a positive and significant unidirectional long‐run causality running from tourism to the economic growth of Kerala and a bidirectional causal relationship between tourism development and the economic growth in the short run. [ABSTRACT FROM AUTHOR]

Purpose: This study investigates the causal connections and both short-term and long-term associations among corruption, investment, unemployment, and per capita economic growth across twenty-two Asia-Pacific nations spanning from 2012 to 2020. Design/Methodology/Approach: The research employs Granger causality and Vector Error Correction Model methodologies to tackle the research inquiries. Findings: The empirical results unveil bidirectional causality between corruption and per capita economic growth, whereas the unemployment rate and per capita economic growth share a unidirectional relationship. Conversely, no causal linkage is found among the remaining variables. In the short run, corruption has no significant impact on per capita economic growth and unemployment but does significantly and adversely affect the investment rate. On the other hand, in the long run, corruption significantly and negatively influences per capita economic growth. The investment rate and unemployment, in the long term, exhibit a substantial and positive influence on per capita economic growth. [ABSTRACT FROM AUTHOR]

The business model and consequently, the bank's risk exposure significantly depends on the source of capital (Riabichenko et al., 2019). This research uses vector error correction model (VECM) data analysis to investigate the influence of capital adequacy ratio (CAR), non-performing loans (NPL), loan to deposit ratio (LDR) on the level of credit distribution at commercial banks in Indonesia. Using secondary data, research data was processed using the EViews 12 application with the research population being banking companies listed on the Indonesia Stock Exchange in 2019-2021. The research results show the variables CAR, NPL, and LDR have a significant effect on long-term credit distribution. In addition, the NPL variable significantly influences the credit distribution variable in the short term. The Granger causality test result shows that there is no two-directional causality relationship between the independent variables CAR, NPL, and LDR on the credit distribution variable. The results of this research are in accordance with financial intermediation theory, where the theory explains that savings and loans with high leverage can reduce the possibility of default (payment failure). [ABSTRACT FROM AUTHOR]

This study explores the link between financial inclusion and food security in Algeria from 2003 to 2022. Using a composite financial inclusion index and the Vector Error Correction Model (VECM), we analyse the data, subjecting it to various diagnostic tests. Surprisingly, our results reveal that financial inclusion (FI) has a significant and positive impact on undernourishment prevalence, indicating a negative effect on food security in both the short and long term. Likewise, food imports (FIM) contribute to higher undernourishment prevalence, implying a weakening of food security in the long-run. Conversely, unemployment rate (UEM) and food production (FOP) show no substantial long-term impact on food security, although UEM has an opposing effect in the short run, meaning it improves food security at the short term; which can be attributed to the informal economy and other State’s policies. Notably, income per capita (INCAPITA) negatively affects undernourishment prevalence, improving food security. These findings offer a nuanced understanding of the complex relationship between financial inclusion and food security in Algeria, emphasizing the need for multifaceted, context-specific policies to address the country's unique challenges.

This study empirically investigates the crowding effect of Foreign Direct Investment (FDI) on domestic investments in Bangladesh, utilizing annual time series data from 1972 to 2022. Initially, unit root tests are conducted with and without considering structural breaks in the dataset. This study employs the Johansen test of cointegration to investigate the enduring association between the variables and utilizes the Vector Error Correction Model (VECM) to accommodate this relationship over the long term. Following the estimation of the VECM, formulas about the magnitude of the crowding effect (CE) are applied to examine the impact of FDI on domestic investment in Bangladesh. Results indicate that FDI positively influences domestic investments in both the short and long run.

This study analyzes the effect of Gross Fixed Capital Formation (GFCF), Imports, Exports, and Government Expenditure of selected G20 member countries on Gross Domestic Product (GDP) using historical data from 1981 to 2021. The detailed analysis aims to explore the relationship between short-term and long-term causality that begins with examining and testing the degree of integration, Unit Root Test, Johansen cointegration test, and causality test. The Vector Error Correction Model (VECM) test results with a 95% confidence interval show that Gross Fixed Capital Formation causes Australia’s and South Africa’s long-term GDPs to have reached a balance point. In addition, Government Spending also causes the European Union’s Gross Domestic Product to achieve a balance point. Imports affect the GDP of the United States, China, and South Africa towards a balance point, and exports affect the GDP of Australia, China, and South Africa. The test results using VECM also conclude that GDP, GFCF, exports, and imports affect GDP growth in the short term. However, on the contrary, on the Australian continent, only GDP, GFCF, and imports which in the previous year had an impact on Australia’s GDP in the short term—concluded that differences in government policies in each country in regulating the economy could affect the causal relationship between the independent variable and GDP in the short and long term.

This study investigated the long-run and short-run relationships between solid waste generation in Nigeria and two key climate variables: rainfall and temperature. Employing a Vector Error Correction Model (VECM) analysis on data from 1982 to 2022, then revealed counterintuitive findings. In the long run, lagged rainfall exhibits a negative association with solid waste (p < 0.05), potentially explained by increased waste decomposition in wetter conditions. Conversely, lagged temperature showed a positive association (p < 0.05), aligning with theories of increased consumption and economic activity in warmer periods. The shortrun analysis unveils a self-correcting mechanism in solid waste generation and a statistically significant negative impact of lagged temperature (p < 0.05), requiring further investigation. Based on these findings, the study proposed policy implications for waste management strategies and data collection, emphasizing the need for sustainable solutions in the context of climate change. [ABSTRACT FROM AUTHOR]

Copyright of Acta Scientiarum Polonorum. Oeconomia is the property of Wydawnictwo SGGW and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we are interested in modeling the dynamics of cutaneous leishmaniasis (CL) in Errachidia province (Morocco), using epidemiologic data and the most notable climatic factors associated with leishmaniasis, namely humidity, wind speed, rainfall, and temperature. To achieve our objective, we compare the performance of three statistical models, namely the Vector Auto-Regressive (VAR) model, the Vector Error Correction model (VECM), and the Generalized Linear model (GLM), using different metrics. The modeling framework will be compared with the Markov Switching (MSM) approach. [ABSTRACT FROM AUTHOR]

Understanding how international trade, FDI and human capital (FDI-HC and ET-HC) in the ICT industry affect Taiwan's stable economic growth between 2001 and 2020 is the main objective of this study. The empirical analysis method used in this study is mainly divided into two steps: First, it uses variables with reliability and authenticity as keywords for primary, data mining, and semantic network analysis (SNA). Second, it investigates the long- and short-term interactions between the variables using the vector error correction model (VECM). The results of data mining and SNA using FDI and ET as keywords reveal that terms connected to HC have high levels of centrality, clustering, and frequency. This finding implies that the variables FDI-HC and ET-HC are reliable and can be utilized as interaction variables. Moreover, FDI–HC and ET–HC exert positive short- and long-term influences on GDP, and ET–HC exerts strong mid- to long-term impacts on GDP, FDI–HC, and ET. [ABSTRACT FROM AUTHOR]

The goal of this study is to detect linear and nonlinear causal pathways toward climate change as measured by changes in global mean surface temperature and global mean sea level over time using a data-based approach in contrast to the traditional physics-based models. Monthly data on potential climate change causal factors, including greenhouse gas concentrations, sunspot numbers, humidity, ice sheets mass, and sea ice coverage, from January 2003 to December 2021, have been utilized in the analysis. We first applied the vector autoregressive model (VAR) and Granger causality test to gauge the linear Granger causal relationships among climate factors. We then adopted the vector error correction model (VECM) as well as the autoregressive distributed lag model (ARDL) to quantify the linear long-run equilibrium and the linear short-term dynamics. Cointegration analysis has also been adopted to examine the dual directional Granger causalities. Furthermore, in this work, we have presented a novel pipeline based on the artificial neural network (ANN) and the VAR and ARDL models to detect nonlinear causal relationships embedded in the data. The results in this study indicate that the global sea level rise is affected by changes in ice sheet mass (both linearly and nonlinearly), global mean temperature (nonlinearly), and the extent of sea ice coverage (nonlinearly and weakly); whereas the global mean temperature is affected by the global surface mean specific humidity (both linearly and nonlinearly), greenhouse gas concentration as measured by the global warming potential (both linearly and nonlinearly) and the sunspot number (only nonlinearly and weakly). Furthermore, the nonlinear neural network models tend to fit the data closer than the linear models as expected due to the increased parameter dimension of the neural network models. Given that the information criteria are not generally applicable to the comparison of neural network models and statistical time series models, our next step is to examine the robustness and compare the forecast accuracy of these two models using the soon-available 2022 monthly data. [ABSTRACT FROM AUTHOR]

The type of relationship that can link energy with economic growth plays a major role in determining the macroeconomic policy of a country. Therefore, several studies have been carried out to derive econometric models to link energy consumption with gross domestic product (GDP). However, in these studies the energy consumption has been used in its global term, while this consumption includes all the economic sectors that use energy (residential, industry, transport, agriculture). Therefore, the objective of this work is to examine this relationship between the gross domestic product (GDP) and energy consumption but disaggregated by sector (residential, transport and industrial). And to validate our model we have taken the case of Morocco during the period 1997-2019, in order to draw the impact of each sector on economic growth. In order to test this causality, a Vector Error Correction Model (VECM) is applied instead of a Vector Autoregressive Model (VAR), using the Johansen cointegration technique. The results obtained showed that in the long run energy consumption by the transportation and residential sectors has a positive impact on GDP, while that of households has a negative impact.

This paper aims to evaluate the human capital on economic growth impact in Romania. Variables have been selected according to an endogenous growth model basing on including the human capital in the Cobb-Douglas production function (Lucas, 1988). As all over usual, here gross domestic product (GDP) will be the endogenous of gross fixed capital formation (GFCF, as physical capital stock), employment (as labour), life expectancy and secondary enrolment rate(as proxies for human capital). We also use expenditure in research and development (R&D) sector (as its percentage in GDP), as control variable. Once our model developed, variables are found as integrated of order one (1) and co-integrated, here allowing a vector error correction model (VECM) for estimation. This will be a system of six equations covering a 25 years (1995-2019) interval for Romania. A long-term relation comes out of our empirical findings, as similarly to Wang (2016), so the GDP growth sees itself determined by: secondary school enrolment, life expectancy(i.e. for human capital), R&D expenditure and labour. Short run causalities have not been found significant for this model

We propose a novel framework to examine relationships among drivers of economic growth in Southeast Asia, a region poised to become a significant manufacturing destination. However, unbalanced economic growth among countries poses risks to multinational companies considering offshoring decisions. Our two-stage framework uses multi-way cointegration analysis and a vector error correction model (VECM) to investigate critical drivers of economic growth. We apply a QVAR model to evaluate dynamic connectedness and spillover effects of offshoring decisions. Using World Bank data, our results show that Southeast Asian countries are interconnected through complex relationships featuring multi-way cointegration and dynamic connectedness, informing evidence-based policy. • A conceptual framework to identify spillover effects on drivers of economic growth. • A multi-way co-integration, causality, and dynamic connectedness analysis. • A quantile vector autoregressive (QVAR) model to assess dynamic connectedness. • A subset of Southeast Asian countries have spillover effects on each other. • A sensitivity analysis of spillover effects across a sequel of quantiles. [ABSTRACT FROM AUTHOR]

Monetary policy is closely related to activities to achieve economic growth, which eventually gives welfare to the community. This study aims to analyze the description of the transmission flow of financing channels, the effect of monetary policy instruments, and their effectiveness to achieve economic growth. The variables used are Islamic Banking Finance (FIN), return of Sharia Bank Indonesia Certificate (SBIS), return of PUAS, and Industrial Production Index (IPI). This study used Vector Error Correction Model (VECM) to determine short- and long-term relationships using the time series data. First, the result of the study showed that the transmission flow could not be identified clearly, because the flow stopped in FIN, and it could not affect IPI, according to the Granger Causality test. Second, the result of VECM estimation showed that all variables only affected long term period and did not affect the short-term period. Third, monetary policy transmission of Islamic banking financing channel was not effective enough, which was proven with the result of IRF simulation, which showed that the effect of shock on financing channel variable (FIN) towards IPI was subsided and stable in the 10th period later. Meanwhile, the result of the FEVD simulation showed that the financing channel variable (FIN) only gave a contribution of as much as 0.14 percent towards IPI. The contribution and policy implications are also discussed in this study.

The aim of this study is to estimate the linkages between the actual guaranteed price of wheat and its yield in Tehran province during 2000-2018 by considering the dummy variables of development programs of the Islamic Republic of Iran. This study used the Augmented Dickey-Fuller stationary test, the Johansen Cointegration test, and the Vector Error Correction Model (VECM) to achieve the aim. The results show that the actual guaranteed price of wheat and its yield are of grade I (1), and based on the Johansen test, the long-run relationship between them is confirmed. The results of the VECM model show that the actual guaranteed price of wheat has a positive and significant effect on wheat yield in Tehran province. By a one percent increase in the actual guaranteed price of wheat, the yield will increase 1.07 percent. Also, the coefficient of vector error correction indicates that if in the short-run occurs a sudden shock to the actual guaranteed price of wheat, it will take about 2 periods to adjust the effect of this shock. Considering that the yield is elastic to the actual price of wheat, suggesting to estimate the guaranteed price of wheat, based on actual price (deflated) in the purchasing policy, can encourage the improvement of wheat yield in the country's agricultural lands. [ABSTRACT FROM AUTHOR]

This paper aims to empirically analyze the relationship between natural disaster, health spending, urban population, gross fixed capital formation, and gross domestic product (GDP) per capita for lower middle-income countries. The data cover the period 2000–2019. The methodological approach used is based on Granger causality and Vector Error Correction Model (VECM) procedures. Empirical result reveals that GDP per capita and health spending are correlated positively with urban population. The results also indicate that there is a one-way relationship running from natural disaster to GDP per capita and from natural disaster to health spending in short and long run, while two-way relationship between health spending and urban population in short term. In long run, there is two-way relationship between GDP per capita and health spending. [ABSTRACT FROM AUTHOR]

In this paper, we are interested in modeling the dynamics of cutaneous leishmaniasis (CL) in Errachidia province (Morocco), using epidemiologic data and the most notable climatic factors associated with leishmaniasis, namely humidity, wind speed, rainfall, and temperature. To achieve our objective, we compare the performance of three statistical models, namely the Vector Auto-Regressive (VAR) model, the Vector Error Correction model (VECM), and the Generalized Linear model (GLM), using different metrics. The modeling framework will be compared with the Markov Switching (MSM) approach.

The dynamic IS equation implies that if the real interest rate is I(1), then so is the output growth rate with possible cointegration, and log output is I(2). This paper extends the Beveridge–Nelson decomposition to such a case, and develops a Bayesian method to obtain error bands. The method is valid whether log output is I(1) or I(2). The paper applies the method to US data to estimate the natural rates (or their permanent components) and gaps of output, inflation, interest, and unemployment jointly, and finds that allowing for cointegration gives much bigger estimates of the gaps for all variables. [ABSTRACT FROM AUTHOR]

The relationship between the housing market, stock market and macroeconomic variables has long been a topic of concern to both academics and practitioners. This paper examines the short-run dynamics and long-run relationships between the residential property price index and the stock market index and four selected macroeconomic variables in Hong Kong. The Johansen (1991) cointegration approach and the vector error correction model (VECM) approach are used to examine the monthly time series during the sample period from 2004 to 2019. Our results show that there is a cointegration relationship between the residential property price index and the stock market index and selected macroeconomic variables. There is evidence that the Hang Seng Index, money supply (M3), total loans and unemployment rate are significantly associated with the residential property price index, while the consumer price index has no significant impact on the residential property price index in the short-run dynamics. Also, only the Hang Seng Index and two macroeconomic variables have a long-run cointegration relationship with the housing market. This is the first attempt to shed light on both short-run and long-run relationships between two capital markets and macroeconomic variables in the context of Hong Kong. Our findings provide important implications for relevant government departments to stabilise the housing market and help practitioners form effective investment strategies. [ABSTRACT FROM AUTHOR]

The relationship between public expenditure and economic growth is obvious, but the direction of causality is not clear. This paper analyses the relative impact of the different components of public expenditure on economic growth. Specifically, this paper examines whether the level of government expenditure is managed to accelerate economic growth or whether government expenditure is used excessively, which may hurt domestic economy because of increased taxes and/or high government borrowing. The vector error correction method is applied to the annual time series data for India from 1983 to 2020 for testing the long- and short-run causality. The pair-wise Granger causality test indicates one-way causality moving from gross domestic product to total government expenditure, and from gross domestic product to government revenue showing that the growth of the economy leads to an increase in both government revenue and expenditure. The estimated error correction coefficient is significantly negative, indicating that the speed of adjustment between the short-run dynamics and the long-run equilibrium is about 0.03%. The results show a stable long-run relationship between public expenditure and economic growth. [ABSTRACT FROM AUTHOR]

This study empirically investigates the crowding effect of Foreign Direct Investment (FDI) on domestic investments in Bangladesh, utilizing annual time series data from 1972 to 2022. Initially, unit root tests are conducted with and without considering structural breaks in the dataset. This study employs the Johansen test of cointegration to investigate the enduring association between the variables and utilizes the Vector Error Correction Model (VECM) to accommodate this relationship over the long term. Following the estimation of the VECM, formulas about the magnitude of the crowding effect (CE) are applied to examine the impact of FDI on domestic investment in Bangladesh. Results indicate that FDI positively influences domestic investments in both the short and long run., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

• EKC, Porter Hypothesis, and Jevons' Paradox are tested in China IS industry. • Cointegration, VECM, IRF and granger causality techniques are used for analyses. • Environmental regulation exerts a critical role in emissions reduction. • The three hypotheses are confirmed under certain conditions. • Policy recommendations for IS industrial low-carbon development are posited. By comprehensively evaluating the potential effectiveness of environmental regulations and technical innovation in facilitating emission reductions, this study highlights the complexity of the relationships — short- and long-term as well as dynamic responses — between carbon dioxide (CO 2) emissions, energy efficiency, economic performance, environmental regulation and technological innovation using the vector error correction model (VECM) by incorporating exogenous policy factors based on China IS industrial data during the period 1995–2017. Empirical analysis indicates: (a) the existence of the Environmental Kuznets Curve (EKC) Hypothesis is established given an inverted U-shaped curve of CO 2 emissions along with the increase of industrial output value; (b) weak Porter Hypothesis (pH) stands in the short term as innovation can be spurred by environmental regulation, whereas strong pH is supported in both the short and long term when energy efficiency and emission reduction can be achieved under strict regulations;(c) Jevons' Paradox is confirmed since the emissions increment brought by the massive increase in demand is greater than the emission reduction volume brought by energy efficiency improvement and; (d) Environmental regulation exerts a critical role in emission reduction, especially for the policies with market-based and common-and-control functions implemented since 2006. Corresponding policy implications to facilitate low-carbon transition of the ISI are proposed. [Display omitted] [ABSTRACT FROM AUTHOR]