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Objective: To describe the evolution of physical activity (PA) research in Africa, examine income and gender inequalities, and discuss future possibilities. Methods: A secondary analyses of the Global Observatory for Physical Activity data on PA research in Africa (1950–2019). Results: We identified 514 PA articles from 47 African countries in the past 70 years. Majority (83.1%) of the articles were published between 2012 and 2019. Fifteen countries had no publications. Six countries (South Africa [n = 156], Nigeria [n = 85], Ethiopia [n = 44], Ghana [n = 41], Kenya [n = 39], and Cameroon [n = 20]) accounted for about 75% of the publications. Most articles were observational (92.4%), single-country studies (78.4%), with male first (58.4%) and last authors (68%), and were classified as surveillance studies (45.1%). Few studies addressed interventions (5.8%) and policy (3.5%) or used device-based PA measurement (14.0%). The number of articles per country was positively related to human population level (r =.552, P =.000) and gross domestic product % spent on research and development (r =.301, P =.040). The publication rate per 100,000 people was positively related with the human development index (r =.349, P =.016) and negatively with the gender inequality index (r = −.360, P =.019). Conclusions: Our results provide an overview and status of PA research in Africa, highlighting country differences and gender inequalities in authorship. The findings may be used to benchmark the evolution of research in the region and to inform areas for improvement. There is an urgent need for more PA interventions and policy studies in Africa. [ABSTRACT FROM AUTHOR]

The paper aims to assess the macroeconomic impacts of government investment in Research and Development (R&D) and more generic fiscal policies by quantifying the Gross domestic product (GDP) and business R&D investment multipliers. Following the recent literature on fiscal policy, we combine the Local Projection approach with fiscal shocks estimated using Structural Vector Autoregressive modeling by focusing on a panel of 15 Organization for Economic Co-operation and Development (OECD) countries for the 1981–2017 period. Our findings support the idea that expansionary fiscal policies can positively and persistently affect the GDP level and crowd in business R&D investment. Additionally, our results show that public investment in R&D generates the largest multiplicative effect both on GDP and business R&D than the one associated with more generic public expenditures, even when fiscal expectations are considered. [ABSTRACT FROM AUTHOR]

Disequilibrating macro shocks affect different firms' prospects differently, increasing idiosyncratic variation in forward-looking stock returns before affecting economic growth. Consistent with most such shocks from 1947 to 2020 enhancing productivity, increased idiosyncratic stock return variation forecasts next-quarter real GDP growth, industrial production growth, and consumption growth both in-sample and out-of-sample. These effects persist after controlling for other leading economic indicators. [ABSTRACT FROM AUTHOR]

Ecosystems generate a wide range of benefits for humans, including some market goods as well as other benefits that are not directly reflected in market activity1. Climate change will alter the distribution of ecosystems around the world and change the flow of these benefits2,3. However, the specific implications of ecosystem changes for human welfare remain unclear, as they depend on the nature of these changes, the value of the affected benefits and the extent to which communities rely on natural systems for their well-being4. Here we estimate country-level changes in economic production and the value of non-market ecosystem benefits resulting from climate-change-induced shifts in terrestrial vegetation cover, as projected by dynamic global vegetation models (DGVMs) driven by general circulation climate models. Our results show that the annual population-weighted mean global flow of non-market ecosystem benefits valued in the wealth accounts of the World Bank will be reduced by 9.2% in 2100 under the Shared Socioeconomic Pathway SSP2-6.0 with respect to the baseline no climate change scenario and that the global population-weighted average change in gross domestic product (GDP) by 2100 is -1.3% of the baseline GDP. Because lower-income countries are more reliant on natural capital, these GDP effects are regressive. Approximately 90% of these damages are borne by the poorest 50% of countries and regions, whereas the wealthiest 10% experience only 2% of these losses.

An economy's primary objective is to foster high economic growth, ultimately enhancing the well-being of its citizens. Recent economic strategies have increasingly emphasized trade-oriented policies to achieve this goal. This study delves into the nexus between Saudi seaborne trade (measured in tons) and gross domestic product (GDP), aiming to determine any causal relationship between these economic variables. Using datasets from the Saudi Central Bank and the General Authority of Statistics, this research employs rigorous unit root, cointegration, and causality tests to find the intricate interplay between trade and economic growth in Saudi Arabia. The empirical analysis highlights a robust long-term relationship between Saudi GDP and seaborne trade, suggesting cointegration. Specifically, the findings reveal a significant impact of economic growth on seaborne trade, as indicated by a trade elasticity of 1.08, which implies that changes in GDP yield a substantial shift in trade. For instance, a 1% increase in GDP corresponds to an approximate 1.08% increase in trade volume. The error correction mechanism also illustrates the dynamic adjustment process: deviations of Saudi seaborne trade and economic growth from equilibrium are corrected by 61% and 43%, in the subsequent year. Furthermore, Granger causality tests unveil bidirectional causality between GDP and trade, elucidating that economic growth changes precede trade volume alterations and vice versa. Thus, fostering international trade not only stimulates income but also bolsters the domestic economy of Saudi Arabia. [ABSTRACT FROM AUTHOR]

Materialism is fundamental to the human value or goal system; therefore, an understanding of its level among Chinese college students and its changes over time is of great value. In the present study, a cross‐temporal meta‐analysis was performed by reviewing studies that conducted Material Values Scale‐based assessment of the materialism level among Chinese university students from 2007 to 2020. Moreover, a time lag analysis was performed to clarify whether variations in materialism level are interpretable with macro‐social indicators. Finally, 82 articles on studies enrolling a total of 45,966 Chinese university students were reviewed. The materialism score significantly increased on a yearly basis. Furthermore, macro‐social changes in diverse areas, including economic condition (gross domestic product per capita, consumption level of all residents and national disposable income per capita), social connectedness (urbanisation degree and divorce ratio) and overall threat (rate of university enrollment), were the major factors influencing the degree of materialism among the students. By identifying the inclining trend of materialism among these college students across time and using relevant macro‐social indicators, a theoretical three‐dimensional framework was established to elucidate the degree of materialism among Chinese college students as a group. [ABSTRACT FROM AUTHOR]

Clarifying the relationship between economic development and human well-being is conducive to promoting high-quality economic development. This study focused on 16 prefecture-level cities in the Qinghai-Xizang Plateau region. The critic weighting method assessed the 2007–2018 human well-being index (HWI). The Tapio decoupling model allowed the study of the human well-being decoupling state. Finally, the drivers of decoupling between economic development and human well-being were analyzed using the Logarithmic Mean Divisia Index method. The results indicated that (1) almost all cities in the study region had an upward 2007–2018 HWI trend, but there were significant differences in growth magnitude and change trend. (2) Economic development and human well-being in the study region in 2007–2018 had expansion negative decoupling, thus, human well-being increased with economic growth, but not as fast as gross domestic product. 9 cities in 2007 showed weak decoupling, expansion connection, and expansion negative decoupling, increasing to 13 cities by 2018, indicating that human well-being development gradually improved from 2007 to 2018. (3) For most cities, the economic scale effect was the most influential factor in the decoupling of economic development and human well-being. Therefore, this study provided policy recommendations for decoupling economic development and human well-being. [ABSTRACT FROM AUTHOR]

Existing research indicates that political stability is a prerequisite for good governance and economic development in Central Asia. All five countries in the region are authoritarian regimes but it is unclear which factors impact on political stability. Using data for the period 2002–2022, this paper examines the variables which make a significant contribution to political stability and finds that the rule of law, personalist rule, a free media, freedom of assembly, political polarization and gross domestic product (GDP) growth, respectively have a positive effect on political stability while government accountability, exclusions by social group and civil society participation have a negative effect. Some of the findings are at odds with other studies on autocratic regimes, illustrating the importance of geo-political context. [ABSTRACT FROM AUTHOR]

This paper quantifies the economic impact of regime changes and macroeconomic indicators on debt stress in Zambia using the Autoregressive Distributed Lag (ARDL) Bounds test. A 1% short run increase in gross domestic products (GDP) increases debt stress by 3.16% and in the subsequent year lowers it by 7.21%; in the long-run the 1% GDP increases lowers debt stress by 22%. In the long-run, a 1% rise in inflation and the lending rate negatively and positively impacted debt stress levels by -1.52% and 3.90%, respectively. Short-run shocks culminated regime change had short-run adverse impact on debt stress by 3.45% in one year and in the subsequent year by -10.35%, with the variables adjusting to long-run equilibrium at a speed of 71.5%. This is the first paper to quantify the empirical effect of macroeconomic indicators and change in Presidents on debt stress, especially in Africa were the problem of the debt trap is perpetuated. The results from the study implies that to deescalate the impact of debt stress on the economy, the electorate should vote in governments that will not fall short on growth driven macroeconomic policies, making it possible for economic sustainability to prevail; and paper seeks to promote good governance and good economic policies as a premise for sustained macroeconomic stability and development. [ABSTRACT FROM AUTHOR]

This paper examines the nexus between carbon dioxide (CO2) emissions, electricity consumption, fossil fuels, foreign direct investment (FDI), gross domestic product (GDP), and renewable energy in the Philippines. This paper also explores the intricate relationships between carbon dioxide (CO2) emissions, electricity consumption, fossil fuel use, foreign direct investment (FDI), gross domestic product (GDP), and renewable energy in the Philippines. Utilizing time-series data from 1990 to 2022 and applying advanced econometric techniques such as vector error correction modeling (VECM) and Granger causality tests, the study reveals the significant impacts of economic growth and energy consumption on CO2 emissions. The findings highlight the crucial role of renewable energy in mitigating environmental degradation. Policy implications are discussed in the context of the Philippines' commitment to sustainable development and climate change mitigation, emphasizing the need for integrated policies that promote renewable energy and energy efficiency alongside economic growth. We use a comprehensive econometric analysis to understand these variables' dynamic interactions and causal relationships. The study employs time-series data from 1990 to 2022 and applies advanced econometric techniques, including vector error correction modeling (VECM) and Granger causality tests. The results highlight the significant impact of economic growth and energy consumption on CO2 emissions while also underscoring the critical role of renewable energy in mitigating environmental degradation. Policy implications are discussed considering the Philippines' commitment to sustainable development and climate change mitigation. [ABSTRACT FROM AUTHOR]

As a metric for economic progress, gross domestic product (GDP) has been criticized for hiding the destruction of natural capital and failing to improve happiness/human well-being. Consequently, many studies unanimously argued for a beyond GDP approach, which led to a proliferation of ensuing metrics. However, the proliferation leads to confusion as it complicates matters for policymakers looking to these metrics to inform coherent development objectives and plans. Our article aims to assess theoretical and empirical efforts to measure human progress based on these notions of well-being and sustainability and critically analyze the strengths and weaknesses of these approaches. We explore systematic trends in indicator activity in the related literature and, in doing so, organize our discussion around the distinct and prominent perspectives that can be identified. We illustrate with a case study on India how divergent matrices, though not beyond comprehension, increase the complexity. [ABSTRACT FROM AUTHOR]

Background: This SRMA reviewed and assessed the changes in the severity of disorders of gut‐brain interaction (DGBI) symptoms during the COVID‐19 pandemic, and evaluated factors associated with symptom severity changes. Methods: Electronic databases were searched until February 2024, for articles reporting on changes in symptom severity in DGBI patients during the COVID‐19 pandemic. The proportion of DGBI patients who reported a change in their symptom severity were pooled using a random‐effects model, and subgroup analyses were conducted to assess the effect of socio‐cultural modifiers on symptom severity in DGBI. Key Results: Twelve studies including 3610 DGBI patients found that 31.4% (95% CI, 15.9–52.5) of DGBI patients experienced symptom deterioration, while 24.3% (95% CI, 10.2–47.5) experienced improvement. Countries with high gross domestic product (GDP) had a 43.5% (95% CI, 16.3–75.2) likelihood of symptom deterioration, compared to 9.2% (95% CI, 1.4–42.2) in lower GDP countries. Similarly, countries with low COVID fatality rates had a 60.1% (95% CI, 19.7–90.3) likelihood of symptom deterioration, compared to 18.3% (95% CI, 7.8–36.9) in higher fatality rate countries. Countries with lenient COVID policies had a 58.4% (95% CI, 14.1–92.3) likelihood of symptom deterioration, compared to 19% (95% CI, 8.2–38.1) in countries with stricter policies. Patients in high vaccine hesitancy countries had a 51.4% (95% CI, 19.5–82.2) likelihood of symptom deterioration, compared to 10.6% (95% CI, 2.7–33.4) in low vaccine hesitancy countries. Conclusions & Inferences: This meta‐analysis reveals that a significantly higher proportion of DGBI patients experienced deterioration of symptoms during the COVID‐19 pandemic. Various sociocultural, economic and environmental factors potentially modify the effects of the COVID‐19 pandemic on DGBI. [ABSTRACT FROM AUTHOR]

This study investigates the factors influencing C O 2 emissions in Romania from 1990 to 2023 using the Autoregressive Distributed Lag (ARDL) model. Before the ARDL model, we identified a set of six policies that were ranked using Fuzzy Electre, Topsis, DEMATEL, and Vikor. The multi-criteria decision-making (MCDM) methods have highlighted the importance of a circular policy on C O 2 emission reduction, which should be a central focus for policymakers. The results of the ARDL model indicate that, in the long term, renewable energy production reduces C O 2 emissions, showing a negative relationship. Conversely, an increase in patent applications and urbanization contributes to higher C O 2 emissions, reflecting a positive impact. In total, five key factors were analyzed: C O 2 emissions per capita, patent applications, gross domestic product, share of energy production from renewables, and urbanization. Notably, GDP does not significantly explain C O 2 emissions in the long run, suggesting that economic growth alone is not a direct driver of C O 2 emission levels in Romania. This decoupling might result from improvements in energy efficiency, shifts towards less carbon-intensive industries, and the increased adoption of renewable energy sources. Romania has implemented effective environmental regulations and policies that mitigate the impact of economic growth on C O 2 emissions. [ABSTRACT FROM AUTHOR]

Whilst many studies have explored academic identity construction, very few take a comparative perspective to examine the various ways of constructing academic identities within and across different disciplines. This paper analyses a key policy document used for evaluating academics' performance along with semi-structured interviews with 37 academics from Chemical Sciences, Medical Sciences, Nursing and Education working in a research-intensive New Zealand university. The use of Foucault's theoretical construct of games of truth provides a novel perspective to investigate the ways in which academics in different disciplines play the academic 'game' and how this might affect their construction of an academic identity. Our analysis suggests that the path into academia is a key factor in their trajectory of academic formation. The study suggests three types of 'valid' academics. It problematises the standardised definition and evaluation of academics and offers contextualised, multiple, dynamic and agential understandings of being and becoming set up through the interplay of forces arising from disciplinary, institutional, professional and personal spheres. [ABSTRACT FROM AUTHOR]

Brexit raised the question of whether the UK will continue to attract internationals. Here the focus is on academic staff – a critical component of the "War for Talents" discourse and current geopolitics in the field. Despite a clear trend of loss of EU internationals, at least among western EU countries, the UK more than compensates for this fall with extra-EU internationals. This is even more evident among younger generations. However, the most notable effect, also having a long-term impact as far as it deals with newer generations, is about average quality of such talents (in this study: salaries at parity of age). Brexit is reducing the capacity to attract/retain the best academics. This happens especially among younger cohorts, and if they come from countries that perform better in GDP per capita, R&D investment, but also national ranking in tolerance and creative class scores. Overall, Brexit is detrimental to the UK in relation to attraction of talents, cutting through a long-term pattern of success. [ABSTRACT FROM AUTHOR]

Tremendous breakthroughs have been developed in Semi-Supervised Semantic Segmentation (S4) through contrastive learning. However, due to limited annotations, the guidance on unlabeled images is generated by the model itself, which inevitably exists noise and disturbs the unsupervised training process. To address this issue, we propose a robust contrastive-based S4 framework, termed the Probabilistic Representation Contrastive Learning (PRCL) framework to enhance the robustness of the unsupervised training process. We model the pixel-wise representation as Probabilistic Representations (PR) via multivariate Gaussian distribution and tune the contribution of the ambiguous representations to tolerate the risk of inaccurate guidance in contrastive learning. Furthermore, we introduce Global Distribution Prototypes (GDP) by gathering all PRs throughout the whole training process. Since the GDP contains the information of all representations with the same class, it is robust from the instant noise in representations and bears the intra-class variance of representations. In addition, we generate Virtual Negatives (VNs) based on GDP to involve the contrastive learning process. Extensive experiments on two public benchmarks demonstrate the superiority of our PRCL framework. [ABSTRACT FROM AUTHOR]

In this study, a hybrid artificial neural network (ANN)-Rao series (Rao_1, Rao_2, and Rao_3) algorithm model was developed to analyze water consumption in Istanbul province, Turkey. A multiple linear regression (MLR) model was developed and an ANN was also trained with back-propagation (BP) artificial bee colony (ABC) algorithms for comparison. Gross domestic product and population data were treated as independent variables. To test the accuracy of the presently developed hybrid model, its outputs were compared with those of ANN-BP, ANN-ABC, and MLR models. Error values calculated for the test set indicated that the ANN-Rao_3 algorithm outperformed the MLR, ANN-BP, and ANN-ABC reference models as well as ANN-Rao_1 and ANN-Rao_2 algorithms. Therefore, using the ANN-Rao_3 model, water consumption forecasts for Istanbul province were generated out to 2035 for low-, expected-, and high-water demand conditions. The model-generated forecasts indicate that the water requirements of Istanbul in 2035 will be between 1182.95 and 1399.54 million m3, with the upper-range estimates outpacing supplies. According to low and expected scenarios, there will be no problem in providing the water needs of Istanbul until 2035. However, according to high scenario, water needs of Istanbul will not be provided as of 2033.Therefore, water conservation policies should be enacted to ensure provision of the water needs of Istanbul province from 2033 onward. [ABSTRACT FROM AUTHOR]

Purpose: Most research on tax evasion focused on microeconomic variables revolving around perceptions and decisions of individual taxpayers. However, a new wave of research is now investigating the role of macroeconomic variables in inducing tax evasion. This study adds to the limited studies in this new direction of research. Previous studies found that inflation, low gross domestic product (GDP) growth and gross fixed capital formation causes recession, increases unemployment, raise interest rates, hurts both domestic and foreign direct investments. This study examined the relationship between these variables and estimated tax evasion in Sub-Saharan Africa. Design/methodology/approach: The study adopts a correlation research design with 2,300 data points collected from 23 countries in Sub-Saharan Africa. Specifically, tax to GDP ratio, gross fixed capital formation per GDP and the GDP annual growth report from each country for the period 2011–2020 was retrieved. Generalised least square regression technique was employed to analyse the data due to the presence of heteroskedasticity in the model and random effect was utilized based on the Hausman test. To avoid misspecification and biased result; therefore, all relevant test was conducted including the multicollinearity test. Findings: The results indicate that GDP annual growth and gross fixed capital formation have a significant negative impact on estimated tax evasion in Sub-Saharan Africa. The findings further indicate a negative but insignificant relationship between inflation and estimated tax evasion in Sub-Saharan Africa. The study concludes that both GDP annual growth rate and gross fixed capital formation negatively influence estimated tax evasion and the policy implications in the African continent were discussed. Originality/value: The new findings on the effects of GDP annual growth, growth fixed capital formation and inflation on estimated tax evasion provide novel knowledge that is currently lacking in the current literature, specifically Sub-Saharan African continent. [ABSTRACT FROM AUTHOR]

This paper discusses the economic effects of a potential cut‐off of the German economy from Russian energy imports. We use a multi‐sector open‐economy model and a simplified approach based on an aggregate production function to estimate the effects of a shock to energy inputs. We show that the effects are likely to be substantial but manageable because of substitution of energy imports and reallocation along the production chain. In the short run, a stop of Russian energy imports would lead to an output loss relative to the baseline situation, without the energy cut‐off, in the range 0.5% to 3% of GDP. This paper is part of the Economica 100 Series. Economica, the LSE "house journal" is now 100 years old. To commemorate this achievement, we are publishing 100 papers by former students, as well as current and former faculty. Benjamin Moll is the Sir John Hicks Professor of Economics at the LSE. David Baqaee was an Asst. Prof. of Economics at the LSE. Moritz Schularick obtained his MSc from the LSE. [ABSTRACT FROM AUTHOR]

Land use change (LUC), including deforestation, agriculture, and urbanization, is a major contributor to climate change (CC). This change in land use has impacts on food, water, and energy systems, creating a complex interconnected web of issues. This study aimed to investigate the global link between LUC and CC from 1990 to 2012. Using time‐series data from the World Bank, LUC was represented by irrigated land, arable lands, and forest areas, while CC was represented by CO2 emissions. Moreover, the relationship between economic growth in high‐income and low‐income countries and LUC and CC was examined in this study. Based on the findings of this study, in low‐income countries, the intensity of LUC is higher in comparison with high‐income countries. Meanwhile, CO2 emissions are increasing in middle‐income and high‐income countries. Economic growth is closely related to CO2 emissions in countries with different levels of income. The study indicated that managing land use is of high importance to mitigate CO2 emissions globally. According to the findings, recent LUC has shown more obvious effects on ecological variables than CC. Although LUC is not inherently directly related to CC, humans change land use, especially in terms of land management, to adapt to CC, and these changes will inevitably bring many environmental impacts. This study contributes significantly to advancing the understanding of the complex relationship between CC and LUC, emphasizing the need for integrated approaches in policy development. These measures are crucial to achieving resilience and meeting global CC reduction targets. [ABSTRACT FROM AUTHOR]

The evolution of land use/land cover (LULC) patterns significantly influences the dynamics of carbon storage (CS) in terrestrial ecosystems. In response to future environmental changes, however, most studies fail to synthesize the effects of policy pathways and evolving core driving factors on LULC projections. This article presents a systematic framework to assess the dynamic response of the terrestrial ecosystem CS to future LULC changes. After investigating spatiotemporal characteristics and driving forces, policy effects and future core driving factors are integrated into the improved Markov–future land use simulation model to project LULC across diverse scenarios. Then the Integrated Valuation of Ecosystem Service and Tradeoff model is coupled to explore CS dynamics with LULC changes. This framework was applied to the Weihe River Basin. The finding reveals that the overall proportion of cultivated land, forestland and grassland is above 85% and is significantly influenced by policy effects. Precipitation, temperature, population density and gross domestic product are core driving factors of LULC changes. Equal-interval projection is a viable approach to mitigate policy impacts by avoiding error propagation while coupling future core driving factors to improve LULC projection accuracy. Ecological protection should be emphasized in the future. The rate of increase in CS is 1.25 and 1.63 times higher than the historical trend and economic development scenario, respectively, which alleviates carbon loss from the expansion of built-up land. This research provides a valuable reference for future insight and optimization of ecological conservation strategies. [ABSTRACT FROM AUTHOR]

The quality of nighttime light (NTL) data is an important factor affecting the estimation of gross domestic product (GDP), but most studies do not use the latest NPP/VIIRS V2 annual composite product, and there is a lack of China's GDP estimation products in recent years. To address this problem, this paper studies the NPP/VIIRS remote sensing estimation method for the GDP in mainland China from 2013 to 2023. First, the remote sensing data are preprocessed, and the noise masking method is used to remove outliers. The total amount of NTL, average NTL value, and comprehensive NTL index data are extracted. Combined with the GDP data from the Statistical Yearbook, a fitting model of the GDP and NTL index is constructed. The differences between different GDP estimation models are compared and analyzed, and the optimal model is selected as the estimation model. In addition, through the optimal fitting model, GDP spatial estimation products from 2013 to 2023 are produced. Moreover, the spatiotemporal variation characteristics of the GDP in mainland China are analyzed, with a focus on the spatiotemporal variation of GDP decline regions and the changes in the GDP rankings of provinces and cities. The main conclusions include the following: (1) In the time regression analysis, the linear model MNL has a strong correlation with the GDP, with an R2 of 0.972. This model is selected as the optimal fitting model to calculate the spatial data of the GDP. (2) The spatial distribution of the GDP in mainland China is high in the east and low in the west, and it shows a characteristic of extending from the provincial capital to the surrounding cities. The connectivity between adjacent high-GDP areas continues to increase. (3) From 2013 to 2023, the GDP in most parts of China showed an upward trend, with 98.56% of pixels growing and only 0.99% of pixels declining. The declining pixels are mainly distributed in heavy industrial cities supported by fossil fuel resources, such as Ordos, Daqing, Aksu, etc. (4) Compared with statistical data, the overall difference of the GDP estimated by NTL data is not large, and the relative error is between 0.04% and 1.95%. From the perspective of the GDP ranking of each province, the ranking of most provinces is not much different, fluctuating between ±2. A small number of provinces have large ranking differences due to reasons such as dominant industries and power supply. By spatializing the GDP data of mainland China in the past 11 years, the spatiotemporal changes of the GDP within mainland China were analyzed. The research results can provide support for government economic decisions such as urban development. [ABSTRACT FROM AUTHOR]

The extraction and development of oil sands in Alberta, Canada, present a complex interplay between economic growth and environmental sustainability. Alberta boasts the world's largest concentration of oil sands, with approximately 1.7 trillion barrels of bitumen in place across its three major areas: Athabasca, Peace River, and Cold Lake. Alberta's oil sands, particularly in the Athabasca Basin, hold substantial reserves, necessitating specialized extraction technologies and great environmental challenges. The economic benefits including job creation and contributions to Canada's GDP are juxtaposed with the volatility of oil prices and the environmental costs. This review provides a comprehensive analysis of the geological history, technological advancements, and a brief review of the environmental and socio-economic impact of Alberta's oil sands industry. This paper is to provide a comprehensive report on how the industrial revolution took place from 1980 to the present, detailing all operators, types of industries, project statuses, and oil sand regions. The GIS maps further illustrate a visual representation of the industry's evolution. The analysis concludes with a discussion on the future trajectory of the oil sands industry, underscoring the imperative for sustainable development amid global climate change pressures. [ABSTRACT FROM AUTHOR]

The inclusion of rural smallholder farmers in the export of horticultural products has two major implications: increased household income through a promising global market, which is a catalyst for productivity, cumulatively growing Gross Domestic Product (GDP) and foreign exchange. So far, there are an estimated 2500 smallholder farmers who participate in the export of horticulture in Kenya. As a result, the current study sought to establish the nexus between quality control practices by the small holder farmers and the growth of the horticultural export market in Kenya. The study collected quantitative data using a structured questionnaire from a sample of 334 smallscale horticultural farmers who were randomly selected. Validity and reliability tests were performed before the main study. Then, descriptive and simple linear regression was used to analyze the data. Quality control practices can be measured through the Pre-production measures, Production and post-production control measures. Findings indicated that there was a statistically significant relationship between quality control practices and the growth of horticultural export markets. It was however observed that, there are weak quality control practices among the farmers which lead to non-conformities of products. This implies that the exports from Kenya become less attractive and make the smallholder farmers less competitive. [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]

Four indicators corresponding to the four targets of the European Monetary Union were calculated. The study showed that: (a) concerning the deviation of state's general government deficit/surplus from 3% of gross domestic product (GDP), all member states had reached their target, with the exception of Cyprus, which was slightly under the target, (b) concerning the deviation of state's general government debt from 60% of GDP, half of all European Union (EU) member states did not reach their targets, and there was a lot to be done, especially from the EU15 member states, (c) concerning the deviation of state's inflation rate from the mean of the three states with best results of +1.5%, it was observed that the average value of EU28 member states had reached the final target, mainly due to the performances of the EU15 member states, (d) and concerning the deviation of state's interest rate from the mean of the three states with the best results of +2%, it was observed that the average value of EU28 member states had reached the final target. [ABSTRACT FROM AUTHOR]

Services provided by nature and ecosystem capital are unpriced and their contributions cannot be observed through factor payments. Estimates of nature's gross domestic product contribution are thus based on bottom‐up extrapolations of local ecological valuations or various sectoral dependency assumptions. These estimates are hence partial and wide‐ranging. On the other hand, omission of nature and ecosystem capital in standard growth regressions potentially biases estimated returns to other factors of production. This paper incorporates various categories of natural capital and biodiversity into growth regressions, with annual data covering more than 100 economies over more than two decades. A range of econometric specifications are used, including fixed effect panel regressions, Arellano and Bond, and Pseudo Poisson Maximum Likelihood. The estimates point to natural capital, especially the narrower and specific ecosystem capital, having a sizeable positive elasticity of around one‐third of total gross fixed capital. Ecosystem capital is thus estimated to contribute around USD6–12 trillion (or 10–20 trillion in purchasing power parity terms) annually for the global economy. This estimated contribution is significantly higher than what is implied by existing stock valuations, underscoring the importance of ecosystem capital to sustainable growth. [ABSTRACT FROM AUTHOR]

Multivariate analysis can help to focus on important phenomena, including trend and cyclical movements, but any economic information in seasonality is typically ignored. The present paper aims to more fully exploit time series information through a multivariate unobserved component model for quarterly data that exhibits seasonality together with cross‐variable component correlations. We show that economic restrictions, including common trends, common cycles and common seasonals can aid identification. The approach is illustrated using Italian GDP and consumption data. [ABSTRACT FROM AUTHOR]

Forecasts of professional forecasters are anomalous: they are biased, and forecast errors are autocorrelated and predictable by forecast revisions. We propose that these anomalies arise because professional forecasters do not know the model that generates the data. We show that Bayesian agents learning about hard-to-learn features of the world can generate all the prominent aggregate anomalies emphasized in the literature. We show this for professional forecasts of nominal interest rates and Congressional Budget Office forecasts of gross domestic product growth. Our learning model for interest rates can explain observed deviations from the expectations hypothesis of the term structure without relying on time variation in risk premia. [ABSTRACT FROM AUTHOR]

Tourism development is not only an economic issue. Measuring its quality more comprehensively has important implications for achieving sustainable tourism. China has adopted the principle of High-quality Development to avoid the problems caused by the obsessive pursuit of GDP growth in the past. This study constructs an index system to measure the level of High-quality Development of tourism (HQDT), based on the new development philosophy that including innovation, coordination, green, openness, and sharing. This paper measures the HQDT in Heilongjiang Province from 2005 to 2019, and the results show that the level of HQDT in Heilongjiang Province has grown slowly. This reveals that the quality of the tourism industry has not achieved the same rapid growth as the tourism economy. Innovation have not played a driving role in HQDT, and coordination, green and open are the direct factors restricting the promotion of HQDT. The study initially reveals the feasibility of using HQDT to comprehensively evaluate the quality of the tourism industry. [ABSTRACT FROM AUTHOR]

Introduction: Gross Domestic Product (GDP) is the most well-known and widely used measure of a country's economic health. However, GDP fails to account for the depletion of natural resources and the environmental damage that occurs in the pursuit of economic growth, leading to an incomplete and potentially misleading picture of a nation's well-being. To address this shortcoming, Green GDP (GGDP) is proposed as a more comprehensive indicator that incorporates environmental factors into the economic assessment. This study builds on extensive literature reviews, internationally accepted GGDP accounting methods, and scholarly research to propose a new GGDP calculation model that better reflects a country's sustainable development. Methods: The proposed GGDP model is divided into two main components: natural resource loss and environmental pollution loss. Each component is further broken down into primary factors that are condensed into 13 subcriteria reflecting a country's capacity for sustainable development. Principal Component Analysis (PCA) is utilized to identify the most representative factors from these sub-criteria and to analyze the relationships among GGDP, these factors, and global mean temperature. Additionally, the Integrated Environmental Sustainability Index (IESI) is used to develop a global temperature mitigation prediction model, which considers the impacts of epidemics, sea and land temperatures, and variations in climate across different regions. Results: The analysis shows a 74% probability that positive GGDP growth correlates with temperature changes over a 50-year period, indicating that economic activities measured by GGDP are linked to climate change. The GGDP model reveals significant differences between global GDP and Green GDP, with the latter growing at a much slower rate. This slower growth of Green GDP is primarily due to the declining share of GDP from natural resourcedependent activities, which has fallen from 90% in the 1970s to 80% in 2020. This trend underscores the increasing gap between traditional economic growth and sustainable development, suggesting that as countries continue to rely on natural resources, their overall ecological efficiency declines, environmental pressures increase, and the potential for long-term sustainable development diminishes. Discussion: The findings demonstrate that all factors within the GGDP model are proportional to global temperature, underscoring the significant impact that natural resource utilization and pollution emissions have on economic growth and climate change. The study further evaluates global sustainable development by considering both economic and environmental perspectives. Using Brazil as a case study, the model is applied to assess the values of each component within the GGDP framework, providing a comprehensive analysis of the country's sustainable development challenges and potential solutions. This approach establishes a method for assessing sustainable development that can be adapted for use in other countries, offering a path forward for integrating environmental considerations into economic policies. [ABSTRACT FROM AUTHOR]

The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas genome editing technology holds great potential in advancing the fields of healthcare, agriculture, and basic biology research. This study uses the national innovation system framework to identify key actors in India's CRISPR research landscape and their roles in technology development and diffusion. Bibliographic analysis revealed that almost half of Indian CRISPR research publications are review articles or book chapters. Patentscope analysis showed that only 13.7% of CRISPR-related patent applications are attributed to Indian nationals, indicating international dominance. The Indian CRISPR research ecosystem is primarily supported by the Department of Biotechnology, Government of India. Addressing lack of hands-on learning opportunities, minimal collaboration amongst limited researchers, and absence of dedicated institutes of excellence are crucial to accelerate CRISPR technology innovation in India. We believe these inferences can be extrapolated to other developing countries that spend a similar percentage of their gross domestic product on biotechnology research. [ABSTRACT FROM AUTHOR]