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Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association 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.)

Currently, most of the studies establish the relationship between night-time light intensity (NTLI) and gross domestic product (GDP) only in the temporal dimension or spatial dimension, without combining both of them or considering real spatio-temporal modelling. Moreover, few studies verify the spatio-temporal heterogeneity of the model. To solve the aforementioned problems, this paper is the first to propose using the geographically and temporally weighted regression model (GTWR) for coupling NTLI and GDP. The NTLI derived from NPP-VIIRS satellites and GDP statistics for 14 urban agglomerations in China from 2013 to 2020 were systematically studied by comparing four methods, including OLS (ordinary least squares), GWR (geographically weighted regression model), TWR (time-weighted regression model), and GTWR. It is found that the GTWR model has the highest coefficient of determination. This finding proves that 'the spatio-temporal nature of material movement is inseparable from each other' and verifies the superiority, correctness, and scientific validity of the GTWR model. The implications of this paper include 1) demonstrating the superiority of the GTWR model (i.e. three-dimensional spatio-temporal model) in fitting the relationship between NTLI and GDP of urban agglomerations; 2) solving the problem of insufficient sample size of the single dimension of NPP-VIIRS through the spatio-temporal model; 3) quantitatively detecting the spatio-temporal heterogeneity of urban agglomerations and analysing the high non-stationarity urban agglomerations by GTWR and spatio-temporal cube with spatio-temporal hot spot analysis. [ABSTRACT FROM AUTHOR]

Gross ecosystem product (GEP) is the aggregate economic value of the final goods and services of the ecosystem produced within a given area, providing a potentially effective means of linking nature protection and restoration to development.In this study, we calculated the values and distribution of biodiversity, GEP and gross domestic product (GDP) in the 547 counties of Southwestern China, a biodiversity hotspot. We then divided the region into nine types of area based on spatial correlations between the three indicators.Areas with high biodiversity, GEP per km2 and GDP per capita were mainly found in and around the large cities of Kunming, Chengdu and Guiyang, reflecting a reasonably high degree of coupling coordination between ecosystems and the local economy.Contrary to our hypotheses, areas with high biodiversity do not necessarily have high GEP, and regions with high GDP do not inherently have low biodiversity. This suggests that synergies exist between biodiversity conservation and economic development, and that increasing the value of material and non‐material services in GEP could improve this alignment.Based on our findings, we argue that GEP can act as a bridge between biodiversity conservation and economic development. Understanding the interactions between biodiversity, GEP and GDP can inform the design of effective policies. In particular, we suggest that sustainable agricultural intensification and forestry along with ecotourism offer promising pathways for advancing nature‐positive development in this ecologically critical region. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

This study examines the combined and individual effects of gross domestic product (GDP), microfinance loan disbursement, per capita power consumption, and total energy consumption on poverty rate in Bangladesh by using annual time series data over the period of 1995–2022. This study determines the direction of causality by applying the Toda–Yamamoto (T–Y) procedure of the Granger causality test in the vector auto regression (VAR) model given the presence of a mixed order of integration of I(1) and I(2). The results of the stationary tests show that all variables except poverty rate are stationary at the I(1) order of integration, according to both the augmented Dicky–Fuller (ADF) and Phillips–Perron (PP) methods, while the poverty rate shows stationarity at the I(2) order in both methods. The T–Y empirical test result shows that the four independent variables combined affect the poverty rate significantly. Power consumption affects both GDP and microfinance and they have bi-directional causality relationship with each other. Our study shows that GDP and total energy consumption individually contribute to poverty reduction. Based on the findings, it is imperative that national policy makers place a greater emphasis on increased domestic production and the proper utilization of power and energy to reduce poverty rates. Policy implications may include strategies to promote sustainable energy development, improve energy efficiency, and provide equitable access to energy services. [ABSTRACT FROM AUTHOR]

Abstract Gross ecosystem product (GEP) is the aggregate economic value of the final goods and services of the ecosystem produced within a given area, providing a potentially effective means of linking nature protection and restoration to development. In this study, we calculated the values and distribution of biodiversity, GEP and gross domestic product (GDP) in the 547 counties of Southwestern China, a biodiversity hotspot. We then divided the region into nine types of area based on spatial correlations between the three indicators. Areas with high biodiversity, GEP per km2 and GDP per capita were mainly found in and around the large cities of Kunming, Chengdu and Guiyang, reflecting a reasonably high degree of coupling coordination between ecosystems and the local economy. Contrary to our hypotheses, areas with high biodiversity do not necessarily have high GEP, and regions with high GDP do not inherently have low biodiversity. This suggests that synergies exist between biodiversity conservation and economic development, and that increasing the value of material and non‐material services in GEP could improve this alignment. Based on our findings, we argue that GEP can act as a bridge between biodiversity conservation and economic development. Understanding the interactions between biodiversity, GEP and GDP can inform the design of effective policies. In particular, we suggest that sustainable agricultural intensification and forestry along with ecotourism offer promising pathways for advancing nature‐positive development in this ecologically critical region. Read the free Plain Language Summary for this article on the Journal blog.

The research aims to conduct a quantitative and qualitative analysis of urbanization trends in Central Asian countries, considering two aspects: the expansion of urban areas and the intensive increase in population in urban agglomerations. To achieve this goal, an analysis of urbanization trends in Central Asian countries for the years 2010–2022 was conducted. The research results emphasize that the relationship between GDP and the urbanization index is complex and not always straightforward. The analysis of the correlation between GDP and the urbanization index in Central Asian countries highlights the complexity of urbanization processes, which depend on various factors. [ABSTRACT FROM AUTHOR]

Investment in and allocating investment resources to business activities is done through the financial market and the banking credit market is part of this market. The most role of banks in the financial market is to give credit to customers. In this matter the capital adequacy assessment and credit risk are important and their control is considered as one of the key effective factors in bank operation and plays a major role in banks and financial institutes’ stability. So, the aim is to determine the effect of macroeconomic variables (GDP, liquidity, inflation rate and exchange rate) on Capital adequacy, liquidity risk and credit risk. The results showed that macroeconomic variables have a significant effect on banking risks. [ABSTRACT FROM AUTHOR]

The British Historian John Hiden once called pre-WWI Latvia "the Belgium of the East" due to its highly advanced manufacturing, serving the Russian market. The independent Latvian nation-state (1918–1940) lost this status together with the industrial equipment, evacuated in 1915–1916 to mainland Russia and never returned. Inter-war Latvia did attempt to "Denmarkize", i.e., to become an internationally important exporter of food and to re-industrialize. Both its economic success and growth performance are severely contested. According to Roses and Wolf (in: Broadberry S, O'Rourke KH (eds) The Cambridge economic history of modern Europe, vol 2. 1870 to the present. Cambridge UP, Cambridge, pp 183–207, 2010), Latvia's GDP per capita growth rate was the highest among the European countries in 1929–1938. According to the most authoritative expatriate Latvian account by Aizsilnieks (Latvijas saimniecības vēsture, 1914–1945. Daugava, Stockholm, 1968), the growth stagnated in the years of the authoritarian Karlis Ulmanis regime (1934–1940). Roses and Wolf estimates are indirect. Aizsilnieks grounds his conclusions in the methodologically outdated national income estimates from inter-war times. This paper resolves the controversy by providing direct supply-side time series of the gross domestic product (GDP) in 1920–1939, based on a benchmark estimate of Latvia's GDP in 1935 by Norkus et al. (Cliometrica, https://doi.org/10.1007/s11698-022-00260-x, 2022). Main findings: The economic growth continued after 1934, but it was not Europe´s top growth performer in 1929–1938. However, in 1922–1938, its growth performance was the best, also due to a very low baseline after the extreme war-related economic destruction. The "Denmarkization" process was only partially successful because agricultural productivity remained low, and the re-industrialization was mainly import-substitution. We also did find that since 1936, Latvia's GDP per capita grew with substantial stagnation of the sectoral share of agriculture and a re-shift from services to manufacturing. [ABSTRACT FROM AUTHOR]

Global attention to climate change has surged since the advent of the Paris Agreement, intensifying the importance of measuring and managing carbon productivity indicators on a national level. Nevertheless, concerns persist regarding the reliability of such measurements because of inherent discrepancies in implementing and operating national-level carbon productivity indicators, coupled with their inherent uncertainty. This study proposes a multiple regression model to address these issues aimed at refining national-level carbon productivity indicator metrics, accounting for factors such as the gross domestic product and total greenhouse gas emissions by sectors. The objective was to offer insights into enhancing and effectively utilizing current indicators, enabling a more nuanced interpretation of the variation in the carbon productivity indicators across diverse industrial landscapes. This study showed that adjustments of the carbon productivity metrics reflect disparities in emissions across industrial structures, with countries characterized by high emissions from non-service industries showing improving trends. In addition, this paper proposes an auxiliary indicator estimating method for carbon productivity that, when utilized with current methodologies, is more usable to interpret productivity indicators within the context of varying industrial compositions across OECD countries. Moreover, by elucidating the nuances of industrial structures, this study advocates for more sophisticated approaches to interpreting and managing the productivity indicators tailored to the unique economic landscape of each country. Nevertheless, the limitations stemming from data availability underscore the need for further research, particularly in refining the national-level carbon resource productivity indicators analyses and exploring the thematic productivity variations in greater depth. By addressing these gaps, future studies will contribute to a more comprehensive understanding of national-level carbon resource productivity indicators dynamics and reveal targeted strategies for sustainable development. [ABSTRACT FROM AUTHOR]

This study analyses the variables that influence investment in a sample of small, labour-intensive companies in a sector that is highly dependent on external demand and the world economy. The aim is to test the three traditional theories of investment (neoclassical theory, free cash flow theory and agency theory), as well as consider the existence of other variables endogenous and exogenous to the company, in order to obtain a model that is appropriate to the reality of the companies in the sample, which consists of 3859 companies in the Portuguese textile sector, for the period from 2010 to 2022. Although there are many studies on the subject, the sample of companies used is different from the others, presenting a unique perspective for understanding investment dynamics in this type of company. The methodology used involves estimating panel data models using the GMM method. The results show that there is a statistically significant and negative relationship between liquidity and asset turnover and investment, so the free cash flow and neoclassical theories, respectively, are partially verified. The agency theory is not confirmed. Other variables are significant in explaining investment: the debt structure is statistically negative, while the size of the company, the GDP and the interest rate are statistically positive. Return on assets proved not to be statistically significant in explaining investment. To summarise, the study highlights the need for financial strategies adapted to the unique characteristics of small businesses. [ABSTRACT FROM AUTHOR]

The intensity of competition figures as an important factor in increasing economic activity. The intensification of market competition is one of the key goals of economic policy in post-socialist countries. The aim of this paper is to determine the contribution of the intensity of competition to the level of economic activity in these countries. In this article, a panel analysis is carried out on a sample of 22 post-socialist countries for the period between 2006 and 2019. The indicator of the intensity of local competition from the Global Competitiveness Index of the World Economic Forum is taken as a measure of the intensity of competition. The results of the conducted research indicate that the increase in the intensity of competition has a positive effect on economic activity, expressed by the level of gross domestic product (GDP) in the selected post-socialist countries. [ABSTRACT FROM AUTHOR]

Copyright of Central Asian Economic Review is the property of Narxoz University 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.)

After the disintegration of the communist system, post-communist countries have been undergoing development for more than 30 years. The communist regime imposed dictatorial objectives on these countries, impeding natural progress and leaving a significant legacy of economic hardships and disrupting economies. The extent of this impact varied for each country, depending on its socio-economic circumstances at the time of the Soviet Union's collapse or earlier, resulting in differing strategies for overcoming these difficulties. Notably, the Federal Republic of Germany is excluded from the discussion of post-communist countries in this article due to its rapid development after merging with West Germany, making meaningful comparisons with other postcommunist countries unfeasible. The principal strategy employed by post-communist countries to address the substantial impact of the command economy was the transformation of state institutions from the communist era into market-oriented institutions. These efforts were particularly challenging for states that gained independence after the collapse of the communist regime, as they had to establish the socio-economic foundations of their statehood while transitioning to a market economy. While some Eastern European economies had already begun the transition from a closed economy to a market economy before the collapse of the Soviet Union, the fall of the Berlin Wall near the end of the 20th century and the subsequent collapse of the Soviet Union significantly accelerated this process. This transition is described as one of the most significant events of the last decade of the 20th century, signifying a global shift toward a market economy. Following the collapse of the Soviet Union, the countries were divided into two categories. The first category consisted of countries with established institutions such as borders, central banks, and currencies, while the second category, including Georgia, relied on the institutions of the Russian Federation and was left without them after the Soviet Union's collapse. Consequently, like most post-communist countries, Georgia prioritized overcoming the challenges brought about by the transition period and establishing the necessary economic institutions. However, Georgia encountered various difficulties on the path to developing an institutional system compatible with a market economy. For example, Georgia attempted to implement the "shock therapy" that had been successful in Poland under the Balcerowicz plan but was unsuccessful due to the lack of a national currency. In an effort to comprehensively understand the changes in the economic wellbeing of the population of post-communist countries after the collapse of the Soviet Union and to compare these indicators with those of Georgia based on IMF data, this article utilizes graphs to illustrate the GDP dynamics of post-communist countries in nominal and purchasing power parity prices per capita. The analysis of these graphs reveals that the countries that are currently EU member states experienced significant economic growth over the years, while Georgia and other countries underwent much slower growth. To comprehensively understand the dynamics of economic well-being in each post-communist country, the article defines 5 time intervals and conducts calculations for each period and country. The intervals range from the collapse of the communist system to future IMF forecasts (1992-2029):  From the collapse of the communist system to the end of the twentieth century (1992-2000);  The first decade of the current century (2001-2010);  The second decade of the current century (before the spread of COVID-19 in these countries in 2011-2019);  COVID-19 and post-COVID period (2020-2023);  IMF forecast (2024-2029). The calculated average GDP per capita indicators for each period and country, expressed in nominal value and purchasing power parity, exemplify Georgia's gradual economic improvement relative to other post-communist countries. Moreover, to facilitate a clear comparison of Georgia's GDP per capita, calculated according to purchasing power parity, with similar indicators of other post-communist countries, the article presents a well-being ladder-shaped table listing the countries whose data lags behind Georgia's corresponding indicators. Comparisons are made for the first available official data after the collapse of the Soviet Union (1994), the end of the 20th century, and the end of each subsequent decade. The analysis illustrates that Georgia's gross domestic product per capita, as calculated according to purchasing power parity, surpassed only Tajikistan, Kyrgyzstan, and Armenia among postcommunist countries in 1994. However, by the end of the 20th century, it also exceeded the indicators of Uzbekistan and Moldova. According to 2020 data, Georgia's gross domestic product per capita surpassed several countries and continues to exhibit positive trends. Finally, the research shows that in a few years, Georgia's GDP per capita, calculated according to purchasing power parity, will exceed the corresponding indicators of ten post-communist countries (Belarus, Turkmenistan, Ukraine, Albania, Azerbaijan, Moldova, Uzbekistan, Kyrgyzstan, Tajikistan, Armenia). However, it will still lag behind Kazakhstan, the Russian Federation, and EU member states. Although the "ladder of economic well-being of post-communist Georgia" mentioned in the article creates an optimistic mood, specific criteria must be met to end the transitional period in the country. The simple answer to whether the transition period will end in a given country is this: if the EU recognizes that a country with a transition economy is ready to join the EU, then most likely the transition period in that country is over, and its current economic system is ready to join the European market. [ABSTRACT FROM AUTHOR]

Background The gross domestic product (GDP) of Association of Southeast Asian Nations (ASEAN) member states can be used to determine their overall economic growth. In order to increase GDP, all emerging nations in Southeast Asia are stating to depend on foreign direct investment (FDI), the tourism industry, international visitors visiting ASEAN member nations banking credit, and low inflation rates. High inflation is a problem that developed and developing countries will definitely face. One of the problems faced by ASEAN member countries is that most of the member countries are in the developing country category. For ASEAN member countries, high inflation levels will encourage economic instability. GDP is less than optimal because of these classic problems in developing countries so that economic growth is not as planned. The ASEAN Economic Community (AEC), which ASEAN established, is seen as a way to reduce unemployment, alleviate poverty by boosting the travel and tourism industry and increasing investment, as well as to offer or distribute credit to businesses at low interest rates in order to boost GDP. The purpose of this study is to investigate and analyze how the GDP of ASEAN member nations is affected by FDI, foreign visitors, bank credit, and inflation. s: We analyzed panel data for seven ASEAN members from 2002 to 2020. FDI, foreign tourists, banking credit, and inflation are the research factors examined as independent variables that have an impact on GDP, the dependent variable. Results The study’s findings indicate that while inflation has a large negative impact on GDP, FDI, foreign tourism, and bank credit have a considerable positive impact on GDP. Conclusions FDI, foreign tourists, bank credit, and inflation have a significant influence on GDP in ASEAN member countries.

Economic growth is the main focus of the country's development, especially measured through Gross Domestic Product (GDP). In the Indonesian context, the blue economy concept is a promising strategy because of the abundant potential of marine resources. This study aims to analyze the influence of the blue economy on economic growth in Indonesia. Through a literature review, it is revealed that the government has implemented various innovative policies and financial instruments to support the blue economy. The research results show that the blue economy has a significant influence on economic growth. However, the challenge of maximizing the benefits of marine resources while maintaining environmental sustainability remains the main focus. By maintaining the sustainability of marine resources, Indonesia can build a strong and sustainable blue economy, providing long-term economic and social benefits for its people. [ABSTRACT FROM AUTHOR]

Examining the effect of economic factors on government and household sports expenses, it is possible to obtain how these factors can cause changes and developments in sports expenses. This knowledge helps the government, organizations and households to make better and more efficient decisions in the field of sports expenses and use their resources more optimally. In this research, instead of focusing on traditional and common factors that may have been considered in previous studies, we have focused on economic factors and their impact on sports expenses. This innovative approach to sports and economic factors can contribute to a deeper understanding of sports costs and provide new solutions for optimal management of sports costs. Also, most researches in this field have focused on the effect of social and health factors of sports, but in this research, the effect of expenses on the government and the household is considered. This innovative approach can not only be effective in better understanding the effect of sports expenses on eliminating the deficiencies of the sports industry system, but also in providing more effective strategies to reduce sports-related expenses in order to reduce government expenses and increase access to sports for households. Finally, it can be said that sports, as an important activity in society, brings significant costs, which can have a significant impact on government and household costs. Economic factors are also among the factors affecting sports costs, and the analysis of their impact on these costs is examined in this article. By considering the effect of economic factors, it is possible to make better and more optimal decisions in the field of sports expenses and management of available resources for the government and households. Sports has become very important by playing a role in the economy of countries. Therefore, the macro-economic effects of the sports industry, such as the share of sports in the gross domestic product and gross national product or its contribution in creating employment and health of the general public, have led countries to accurately and regularly measure the effects of sports on their country's economy every year. evaluate Therefore, on the one hand, due to the low share of the sports economy in Iran's economy, and on the other hand, due to the lack of identification of factors affecting the development of the sports economy in Iran and the existence of a research gap in this field, conducting research on the sports economy and how to develop and expand its volume It is important and necessary. Therefore, this research has been done in response to the question, what effect do economic factors have on government and household sports expenses? This research was conducted with the econometric method and the short-term and long-term relationships between the variables were examined from the vector autoregression (var) pattern, instantaneous response functions (IRF), and variance analysis (VD) as well as the accumulation test. The test of existing theories in this field, the relationship between economic factors and government and household sports expenses were analyzed in the period from 1363 to 1400. The software used to validate the data was Eviuz version 12. Household sports expenses, within the scope of the Statistics Center questionnaire, are a part of the expenses of recreation, entertainment and cultural services, as well as a part of the expenses in the procurement and sale of durable household goods in the non-food sector, which include: clothes, shoes, learning various sports, providing tickets, entrance and membership fees, equipment rental, all kinds of balls, billiard and ping pong tables and other sports equipment. Also, in order to access the household sports cost information, the results of the cost and income statistics plan of urban and rural households from 1363 to 1400 were used, and in this way, the average sports cost for an urban and rural household was calculated. Then, by using the available information about the number of urban and rural households in the mentioned years, by multiplying the sports expenses of an urban and rural household by the number of existing households and adding them together, the total consumption of household sports was obtained. It is worth mentioning that the statistics of the number of urban and rural households in the country were not available in all the years in question, and the researcher roughly extracted these statistics using the statistics available in the previous and subsequent years. The country's budget is divided into four sections of public affairs, defense, social and economic affairs, which are divided into other chapters and programs, and physical education and youth affairs are placed in the fifth chapter of social affairs. In order to determine the government's expenses in this sector, first the information available in the budget law of the whole country was checked, but due to the fact that the approved budget and the government's performance in this sector are not the same, as well as the lack of figures related to the performance in some years in the budget law, from the information Available in the economic reports of the Management and Planning Organization were also used. Statistics related to GDP, tax rate, liquidity volume, exchange rate, inflation rate, interest rate, trade balance, unemployment-employment rate and total stock market index were extracted from the time series data of Central Bank of Iran. According to the findings, among the identified economic factors, the GDP factor affects the government's sports expenses, and the GDP factor and the inflation rate affect the household sports expenses. According to the findings, among the identified economic factors, the GDP factor has an impact on the government's sports expenses. . An increase in incomes and economic growth can lead to an increase in export earnings and, as a result, an increase in expenses related to sports and the development of athletes in the country. GDP is affected by investment and consumption within the country. The effect of this gross domestic product on sports expenses of the Iranian government is investigated with two possible solutions. A) Increase in GDP: Increase in GDP can lead to economic growth and increase in domestic incomes. This increase in income can lead to an increase in the government's sports budget, and as a result, the government's sports expenses will increase; b) Decrease in GDP: Decrease in GDP can lead to decrease in domestic incomes and economic recession. In this case, the government's sports budget will also decrease and the government's sports expenses will also decrease. In general, an increase in the inflation rate means an increase in prices, and as a result, it can increase household sports expenses. As a result, buying sports equipment or joining a sports club, holding sports activities or attending sports events may become more expensive. This increase in prices may reduce the purchasing power and motivation of households to spend on sports and recreational activities. For example, if the price of sports equipment such as sports shoes or sports clothes increases due to inflation, households may feel uncomfortable buying these products or may not be able to afford them due to the higher cost. It can be said that the effect of the inflation rate on household sports expenses is strongly dependent on the level and growth rate of inflation. Finally, based on the findings, it is suggested that the government increase financial support for athletes by increasing credits and cooperating with companies and sponsors. This includes setting appropriate salaries for professional athletes, paying for training and equipment, as well as support for travel and competition expenses. This action can reduce the expenses of families to support athletes and provide the possibility for athletes to continue professional sports activities. [ABSTRACT FROM AUTHOR]

This study delves into the intricate interplay of economic growth components, specifically focusing on consumption and investment in Saudi Arabia from 2000 to 2022. Employing vector error correction models and co-integration techniques, we analyze the short- and long-term dynamics within the relationship of consumption, investment, and economic growth. Granger causality analysis is also used to discern these pivotal variables' causal connections. Our empirical analysis reveals a persistent long-term cointegration relationship among the variables, underscoring the enduring nature of their interdependency. Furthermore, our findings highlight consumption and investment's statistically significant positive impact on economic growth. Notably, the short-term analysis unveils a stable model characterized by an annual adjustment to equilibrium of 100%. Moreover, the Granger causality study demonstrates unidirectional causal linkages among consumption, investment, and economic growth. These findings hold substantial implications for policy formulation in Saudi Arabia. Policymakers must grasp the ramifications of burgeoning prosperity and evolving private consumption patterns on future environmental outcomes. Achieving sustainable long-term results necessitates equal emphasis on bolstering private consumption and fostering other facets of economic growth. [ABSTRACT FROM AUTHOR]

This research paper conducts a comprehensive analysis of the complex relationship between CO2 emissions and economic factors, specifically investigating the Environmental Kuznets Curve (EKC) theory in Malaysia. Using data from 1991 to 2020, the study applies the Autoregressive Distributed Lag (ARDL) modelling approach developed by Pesaran et al. (2001). The results demonstrate a significant long-term connection between Gross Domestic Product (GDP), trade, and carbon emissions, indicating that economic development plays a crucial role in influencing Malaysia's carbon footprint. Additionally, the inclusion of institutional quality in the model adds another layer of complexity, highlighting the multifaceted nature of the relationship between economic progress and environmental outcomes. Furthermore, examining short-term dynamics using the ARDL model reveals diverse effects over time for variables such as renewable energy and institutional quality, providing a more nuanced understanding of these relationships. These detailed insights are essential for policymakers dealing with the challenges of promoting economic progress while ensuring environmental sustainability. The findings contribute to a deeper understanding of the interplay between economic variables and CO2 emissions, offering valuable guidance for policymakers striving to strike a balance between economic growth and environmental conservation in Malaysia. [ABSTRACT FROM AUTHOR]

This study examines the intricate relationship between investment in research, development, and innovation (RDI) and a nation's gross domestic product (GDP), with a specific focus on understanding how technical and vocational education and training (TVET) performance moderates this association. Utilizing panel data analysis, the research investigates the direct and interactive effects of RDI expenditure and TVET performance on GDP across various national contexts. While the direct impact of RDI on GDP is not statistically significant, the findings illuminate a moderated relationship wherein TVET performance serves as the interaction coefficient. Notably, countries with improved TVET performance demonstrate a significant positive correlation between RDI expenditure and GDP, highlighting the pivotal role of TVET in enhancing the effectiveness of RDI investment for economic progress. This investigation contributes to a deeper understanding of the nuanced interplay among education, innovation, and economic growth, emphasizing the strategic importance of TVET in reinforcing the efficacy of RDI policies for fostering economic advancement. By shedding light on these dynamics, the study provides valuable insights for policymakers, educators, and researchers aiming to design evidence-based strategies for sustainable economic development (Smith & Brown, 2020). [ABSTRACT FROM AUTHOR]

Foreign direct investments are an instrument widely used by countries, both developed and emergent, to achieve their economic development. Among others benefits, discussed in the specialized literature, investment inflows accelerate production, improve productivity, facilitates the transfer of technology, raise the qualification oh human capital, contributes to create new jobs and to boost the economic growth of the host countries. The main purpose of the research is to examine the short and long runs relationships between foreign direct investment (FDI) and economic growth in Algeria. To carry out our investigation, the time series data of the two variables are used over the period 1990-2023. This period covers the span time in which data on the volume of foreign direct investments are available. To achieve the research purpose, an ARDL model is built based on the unit root methodology of the Augmented Dickey Fuller and Phillips  Perron tests. The findings of the study show that the foreign direct investment (FDI) and economic growth are cointegrated, indicating the existence of a long relationship among them. They establish also that foreign direct investment (FDI) has a positive impact on economic growth. Building on the research results, Algerian decision makers should take the required actions to improve the climate business, making it more attractive for foreign investors.

Global-scale remotely sensed nighttime light (NTL) data, such as the Suomi National Polar-orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) Day/Night Band (DNB) NTL data, has been widely applied across multiple disciplines. However, its broader application is still limited by its coarse spatial resolution. We proposed the NTL conditional multiscale downscaling model (NTL-CMDM) for downscaling NPP/VIIRS DNB. The model uses multisource scale factors as conditional constraints, progressively integrating NTL and scale factors to downscale NPP/VIIRS DNB from 500 to 130 m using data from 201 Chinese cities. The downscaled results were validated against the 130 m Loujia1-01 suggest that the NTL data quality was improved after downscaling, yielding higher the coefficient of determination (R: 0.407 versus 0.702) and lower root-mean-square error (RMSE: 7.020 versus 26.424 nWcm−2sr−1) values than those of the original NPP/VIIRS DNB. The downscaled results exhibit richer NTL feature details and show similarity to Luojia-1-01. More importantly, the downscaling enhances the accuracy of NTL statistical metrics, improving illuminated area by 10.23% and radiance estimation by 6.12%. Furthermore, the usability of the downscaled results was assessed by estimating county-level GDP. The GDP estimates based on the downscaled data were superior to those from the original NPP/VIIRS DNB data and consistent with the estimates obtained from Luojia1-01. Finally, generalization ability test using different algorithms in multiple cities demonstrate that NTL-CMDM is robust to cities with different NTL structures. The study verifies the practicability of employing deep learning methods to downscale NTL data, providing a feasible pathway for acquiring high-resolution NTL data over an expanded area.

Background The gross domestic product (GDP) of Association of Southeast Asian Nations (ASEAN) member states can be used to determine their overall economic growth. In order to increase GDP, all emerging nations in Southeast Asia are stating to depend on foreign direct investment (FDI), the tourism industry, international visitors visiting ASEAN member nations banking credit, and low inflation rates. High inflation is a problem that developed and developing countries will definitely face. One of the problems faced by ASEAN member countries is that most of the member countries are in the developing country category. For ASEAN member countries, high inflation levels will encourage economic instability. GDP is less than optimal because of these classic problems in developing countries so that economic growth is not as planned. The ASEAN Economic Community (AEC), which ASEAN established, is seen as a way to reduce unemployment, alleviate poverty by boosting the travel and tourism industry and increasing investment, as well as to offer or distribute credit to businesses at low interest rates in order to boost GDP. The purpose of this study is to investigate and analyze how the GDP of ASEAN member nations is affected by FDI, foreign visitors, bank credit, and inflation. s: We analyzed panel data for seven ASEAN members from 2002 to 2020. FDI, foreign tourists, banking credit, and inflation are the research factors examined as independent variables that have an impact on GDP, the dependent variable. Results The study’s findings indicate that while inflation has a large negative impact on GDP, FDI, foreign tourism, and bank credit have a considerable positive impact on GDP. Conclusions FDI, foreign tourists, bank credit, and inflation have a significant influence on GDP in ASEAN member countries.

In regional development studies, GDP serves as an important indicator for evaluating the developing levels of a region. However, due to statistical methods and possible human-induced interfering factors, GDP is also a commonly criticized indicator for less accurately assessing regional economic development in a dynamic environment, especially during a globalized era. Moreover, common data collection approaches are often challenging to obtain in real-time, and the assessments are prone to inaccuracies. This is especially true in economically underdeveloped regions where data are often less frequently or accurately collected. In recent years, Nighttime Light (NTL) data have emerged as a crucial supplementary data source for regional economic development evaluation and analysis. We adapt this approach and attempt to integrate multiple sources of spatial data to provide a new perspective and more effective tools for economic development evaluation. In our current study, we explore the integration of OpenStreetMap (OSM) data and NTL data in regional studies, and apply a Geographically and Temporally Weighted Regression model (GTWR) for modeling and evaluating regional economic development. Our results suggest that: (1) when using OSM data as a single data source for economic development evaluation, the adjusted R2 value is 0.889. When using NTL data as a single data source for economic development evaluation, the adjusted R2 value is 0.911. However, the fitting performance of OSM data with GDP shows a gradual improvement over time, while the fitting performance of NTL data exhibits a gradual decline starting from the year 2014; (2) Among the economic evaluation models, the GTWR model demonstrates the highest accuracy with an AICc value of 49,112.71, which is 2750.94 lower than the ordinary least squares (OLS) model; (3) The joint modeling of OSM data with NTL data yields an adjusted R2 value of 0.956, which is higher than using either one of them alone. Moreover, this joint modeling approach demonstrates excellent fitting performance, particularly in economically underdeveloped regions, providing a potential alternative for development evaluation in data-poor regions. [ABSTRACT FROM AUTHOR]

In 2023, China's economy maintained a recovery trend after the epidemic. However, the economic recovery showed the characteristics of wave-like development, and it is expected that China's economy will grow steadily and rapidly in 2024. This study introduces a comprehensive approach to forecasting annual GDP growth, integrating input-output models, econometric models, prosperity analysis, expert insights, and scenario analysis. Reviewing China' s economic performance in 2023 and the prevailing global and domestic conditions, it is projected that China' s GDP growth rate will be around 5.3% in 2024. Based on this analysis, several policy recommendations are suggested, namely, intensifying macro-control policies for both cyclical and counter-cyclical phases; elevating the industrial chain's modernization to enhance its resilience and competitiveness; and providing steady support to the growth of small and medium-sized enterprises. [ABSTRACT FROM AUTHOR]

Economic forecasting is crucial since it benefits many different parties, such as governments, businesses, investors, and the general public. This paper presents a novel methodology for forecasting business cycles using an autoregressive integrated moving average (ARIMA), a popular linear model in time series forecasting, and a neural network with weighted fuzzy membership functions (NEWFM) as a forecasting model generator. The study used a dataset that included seven components of the leading composite index, which is used to predict positive or negative trends in several economic sectors before the GDP is compiled. The preprocessed time series data comprising the leading composite index using ARIMA were used as input vectors for the NEWFM to predict comprehensive business fluctuations. The prediction capability significantly improved through the duplicated refining process of the dataset using ARIMA and NEWFM. The combined ARIMA and NEWFM techniques exceeded ARIMA in both classification and prediction, yielding an accuracy of 91.61%. [ABSTRACT FROM AUTHOR]

The regions with the best economy have a greater capacity to develop low-income or social-impact housing, thus contributing to the reduction of poverty and, therefore, to the fulfillment of Sustainable Development Goals. This is observed in fewer people living in extreme poverty and with fewer unmet basic needs. This present article analyzes the correlation between the development in the main economic sectors in different regions (departments) of Colombia and the supply of low-income housing. Nevertheless, the most remarkable relation that was found was between the economic development of the regions (GDP) and the supply of non-social housing (more expensive commercial value) (Spearman's Rho: 0.9). This means that there is an imbalance between regional economic capacity and the low-income housing supply because the regions with higher economic potential should have a less demanding population, that is, people living in poverty. These correlations are better when they go hand in hand with activities that are mostly developed in an urban environment, such as manufacturing, construction, real estate, and finance and insurance. On the contrary, these correlations are worse with industries such as mining and agriculture that mostly operate in rural areas. The analysis between low-income housing and economic sectors' GDP yields low correlations, but these are worse for rural industries. Also, the investigation shows a positive change in the correlations' trend for the year 2021, the beginning of the post-pandemic economic recovery. [ABSTRACT FROM AUTHOR]

The interwar independent Republic of Latvia was among the first ten pioneering states, where a national statistical office published official estimates of total output (1934–1936). Paradoxically, however, Latvia is the Baltic country with the most disputed interwar economic growth performance. According to the authoritative account of Roses and Wolf in The Cambridge Economic History of Modern History (2010), Latvia's GDP per capita growth rate was the highest among European countries in 1929–1938. It impressively ranked number ten according to GDP per capita next to Sweden, France, and Norway. However, according to Norkus and Markevičiūtė (in Cliometrica 15:565–674, 2021. https://doi.org/10.1007/s11698-020-00216-z), it only surpassed Southern European countries, and its growth performance was mediocre. Both these contradictory estimates are derived by indirect methods. This paper contributes to the resolution of this controversy, directly estimating Latvia's GDP in 1935 within the SNA 2008 framework, providing gross value-added calculation for 20 ISIC industries at basic and at market (purchasers') prices. It provides a more fine-grained analysis of the composition of Latvia's total output in comparison with interwar historical national accounts, where only 11 industries were distinguished. This estimate provides the benchmark for future research on Latvia's interwar economic growth performance. Converting our estimates into monetary units, used in the Maddison Project Database, we assess Latvia's position in the international GDPpc ranking, coming to conclusions that dovetail with Norkus and Markevičiūtė (2021) findings. [ABSTRACT FROM AUTHOR]

The unemployment rate and Gross Domestic Product (GDP) are among the most important economic indicators that understanding their current and future trends can help policymakers and decision-makers adopt appropriate solutions to prevent crises and improve the country's economic situation. Accurate prediction of these two indicators can be useful in future planning and improving the country's economy and people's livelihoods. In recent years, artificial intelligence techniques and tools, given their many capabilities, can play a very important role in predicting important economic indicators. Therefore, given the high importance of the two indicators of unemployment rate and GDP on the economy of our country Iran, this article intends to first predict these two indicators separately and then predict the rate of GDP growth based on the unemployment rate using artificial intelligence techniques. For this purpose, in this research, seasonal data related to GDP and its components and the unemployment rate for the years 1976-2022 have been used. Also, machine learning models based on regression have been used for prediction. The results show that the predictions of the mentioned models have an appropriate accuracy in terms of evaluation criteria such as root mean square error, mean absolute error, mean absolute percentage error, which indicates. [ABSTRACT FROM AUTHOR]

Foreign direct investments are an instrument widely used by countries, both developed and emergent, to achieve their economic development. Among others benefits, discussed in the specialized literature, investment inflows accelerate production, improve productivity, facilitates the transfer of technology, raise the qualification oh human capital, contributes to create new jobs and to boost the economic growth of the host countries. The main purpose of the research is to examine the short and long runs relationships between foreign direct investment (FDI) and economic growth in Algeria. To carry out our investigation, the time series data of the two variables are used over the period 1990-2023. This period covers the span time in which data on the volume of foreign direct investments are available. To achieve the research purpose, an ARDL model is built based on the unit root methodology of the Augmented Dickey Fuller and Phillips -- Perron tests. The findings of the study show that the foreign direct investment (FDI) and economic growth are cointegrated, indicating the existence of a long relationship among them. They establish also that foreign direct investment (FDI) has a positive impact on economic growth. Building on the research results, Algerian decision makers should take the required actions to improve the climate business, making it more attractive for foreign investors. [ABSTRACT FROM AUTHOR]

Global attention to climate change has surged since the advent of the Paris Agreement, intensifying the importance of measuring and managing carbon productivity indicators on a national level. Nevertheless, concerns persist regarding the reliability of such measurements because of inherent discrepancies in implementing and operating national-level carbon productivity indicators, coupled with their inherent uncertainty. This study proposes a multiple regression model to address these issues aimed at refining national-level carbon productivity indicator metrics, accounting for factors such as the gross domestic product and total greenhouse gas emissions by sectors. The objective was to offer insights into enhancing and effectively utilizing current indicators, enabling a more nuanced interpretation of the variation in the carbon productivity indicators across diverse industrial landscapes. This study showed that adjustments of the carbon productivity metrics reflect disparities in emissions across industrial structures, with countries characterized by high emissions from non-service industries showing improving trends. In addition, this paper proposes an auxiliary indicator estimating method for carbon productivity that, when utilized with current methodologies, is more usable to interpret productivity indicators within the context of varying industrial compositions across OECD countries. Moreover, by elucidating the nuances of industrial structures, this study advocates for more sophisticated approaches to interpreting and managing the productivity indicators tailored to the unique economic landscape of each country. Nevertheless, the limitations stemming from data availability underscore the need for further research, particularly in refining the national-level carbon resource productivity indicators analyses and exploring the thematic productivity variations in greater depth. By addressing these gaps, future studies will contribute to a more comprehensive understanding of national-level carbon resource productivity indicators dynamics and reveal targeted strategies for sustainable development.

In regional development studies, GDP serves as an important indicator for evaluating the developing levels of a region. However, due to statistical methods and possible human-induced interfering factors, GDP is also a commonly criticized indicator for less accurately assessing regional economic development in a dynamic environment, especially during a globalized era. Moreover, common data collection approaches are often challenging to obtain in real-time, and the assessments are prone to inaccuracies. This is especially true in economically underdeveloped regions where data are often less frequently or accurately collected. In recent years, Nighttime Light (NTL) data have emerged as a crucial supplementary data source for regional economic development evaluation and analysis. We adapt this approach and attempt to integrate multiple sources of spatial data to provide a new perspective and more effective tools for economic development evaluation. In our current study, we explore the integration of OpenStreetMap (OSM) data and NTL data in regional studies, and apply a Geographically and Temporally Weighted Regression model (GTWR) for modeling and evaluating regional economic development. Our results suggest that: (1) when using OSM data as a single data source for economic development evaluation, the adjusted R2 value is 0.889. When using NTL data as a single data source for economic development evaluation, the adjusted R2 value is 0.911. However, the fitting performance of OSM data with GDP shows a gradual improvement over time, while the fitting performance of NTL data exhibits a gradual decline starting from the year 2014; (2) Among the economic evaluation models, the GTWR model demonstrates the highest accuracy with an AICc value of 49,112.71, which is 2750.94 lower than the ordinary least squares (OLS) model; (3) The joint modeling of OSM data with NTL data yields an adjusted R2 value of 0.956, which is higher than using either one of them alone. Moreover, this joint modeling approach demonstrates excellent fitting performance, particularly in economically underdeveloped regions, providing a potential alternative for development evaluation in data-poor regions.

Economic forecasting is crucial since it benefits many different parties, such as governments, businesses, investors, and the general public. This paper presents a novel methodology for forecasting business cycles using an autoregressive integrated moving average (ARIMA), a popular linear model in time series forecasting, and a neural network with weighted fuzzy membership functions (NEWFM) as a forecasting model generator. The study used a dataset that included seven components of the leading composite index, which is used to predict positive or negative trends in several economic sectors before the GDP is compiled. The preprocessed time series data comprising the leading composite index using ARIMA were used as input vectors for the NEWFM to predict comprehensive business fluctuations. The prediction capability significantly improved through the duplicated refining process of the dataset using ARIMA and NEWFM. The combined ARIMA and NEWFM techniques exceeded ARIMA in both classification and prediction, yielding an accuracy of 91.61%.

The regions with the best economy have a greater capacity to develop low-income or social-impact housing, thus contributing to the reduction of poverty and, therefore, to the fulfillment of Sustainable Development Goals. This is observed in fewer people living in extreme poverty and with fewer unmet basic needs. This present article analyzes the correlation between the development in the main economic sectors in different regions (departments) of Colombia and the supply of low-income housing. Nevertheless, the most remarkable relation that was found was between the economic development of the regions (GDP) and the supply of non-social housing (more expensive commercial value) (Spearman’s Rho: 0.9). This means that there is an imbalance between regional economic capacity and the low-income housing supply because the regions with higher economic potential should have a less demanding population, that is, people living in poverty. These correlations are better when they go hand in hand with activities that are mostly developed in an urban environment, such as manufacturing, construction, real estate, and finance and insurance. On the contrary, these correlations are worse with industries such as mining and agriculture that mostly operate in rural areas. The analysis between low-income housing and economic sectors’ GDP yields low correlations, but these are worse for rural industries. Also, the investigation shows a positive change in the correlations’ trend for the year 2021, the beginning of the post-pandemic economic recovery.