Capita Gross Domestic Product Research Articles

Autoregressive Integrated Moving Average Predictive Modelling for Per Capita GDP of Nepal

A nation's Gross Domestic Product (GDP) is an important index that reflects the health and performance of an economy and its aggregate income. In this paper, annual data of Nepal's GDP for the period 1960 – 2022 is used to forecast the GDP of Nepal through Autoregressive Integrated Moving Average (ARIMA) modelling techniques. We seek to make accurate long-term predictions for the period 2023 – 2037 to gain insights into the future expected trajectory of economic growth in Nepal. In the present empirical study, stationarity at the second-order differencing with the ARIMA (2, 2, 1) model is identified to predict the GDP of Nepal for the next 15 years. The finding shows that the forecast values of Nepal’s GDP will be $1384.426 per capita in 2023 and $2180.822 per capita in 2037. Our study provides skeletal guidance for government bodies and investors who rely on planning and strategizing resources on accurate predictions of GDP per capita. By accurately predicting GDP per capita, administrators in investment and policy making can make informed economic decisions that may steer economic growth, stability, and development in an optimum direction.

Large carnivores and the EU LIFE programme

Most large carnivore populations have increased in recent decades in Europe. Along with the adoption of the EU Habitats Directive in 1992, the EU launched a funding program entirely dedicated to environment, nature protection and climate action, the European LIFE programme. Here, we explore the investment of this programme in large carnivore conservation across Europe, from its implementation in 1992 to 2020 (28 years), with respect to budgets, species considered, economic factors and the geographical distribution of the projects. A total of 153 projects have directly or indirectly targeted large carnivores, with a total budget of ca. 338.2 M€ (EU contribution >194.1 M€). Despite the transboundary character of most large carnivore populations, only 5.8 % of projects worked across national borders. Large carnivores did not receive equal attention within the programme, and EU Member States from Southern Europe accumulated 77.7 % of the projects. The region of Abruzzo (Italy) has been granted with the highest number of projects (n= 28), followed by Castilla y León (Spain, 16 projects). At national level, the per capita Gross Domestic Product (GDP) did not correlate with the number of LIFE projects granted, but the proportion of the GDP invested in the protection of biodiversity and landscapes (positively). However, at the level of European regions (NUTS 2), those regions with lower average per capita GDP were more frequently granted with LIFE projects. We discuss several factors affecting the distribution of projects across Europe, and we make recommendations for future investments in maximizing large carnivore conservation under this program.

Real-world cost- effectiveness analysis: Tumor Treating Fields for newly diagnosed glioblastoma in China.

Glioblastoma (GBM) stands as the most aggressive and prevalent primary brain malignancy. TumorTreating Fields (TTFields), an innovative therapy complementing chemotherapy for GBM treatment, which can significantly enhance overall survival, disease progression-free survival, and patient's quality of life. However, there is a dearth of health economics evaluation on TTFields therapy both domestically and internationally. The study aims to assess the cost-effectiveness of TTFields + temozolomide (TMZ) in comparison to TMZ alone for newly diagnosed GBM patients. The intent is to provide robust economic evidence to serve as a foundation for policymaking and decision-making processes in GBM treatment. We estimated outcomes for newly diagnosed GBM patients over a lifetime horizon using a partitioned survival model with three states: Progression-FreeSurvival, ProgressionDisease, and Death. The survival model was derived from a real-world study in China, with long-term survival data drawn from GBM epidemiology literature. Adverse event rates were sourced from the EF-14 trial data. Cost data, validated by expert consultation, was obtained from public literature and databases. Utility values were extracted from published literature. Using Microsoft Excel, we calculated expected costs and quality-adjusted life years (QALYs) over 15 years from a health system perspective. The willingness-to-pay threshold was set at three times the Chinese per capita Gross Domestic Product (GDP) in 2022, amounting to CN¥242,928 (US$37,655) /QALY. A 5% discount rate was applied to costs and utilities. Results underwent analysis through single factor and probability sensitivity analyses. TTFields + TMZ demonstrated a mean increase in cost by CN¥389,326 (US$57,859) and an increase of 2.46 QALYs compared to TMZ alone. The incremental cost-effectiveness ratio (ICER) was CN¥157,979 (US$23,474) per QALY gained. The model exhibited heightened sensitivity to changes in the discount rate. Probability sensitivity analysis indicates that, under the existing threshold, the probability of TTFields + TMZ being economical is 95.60%. This cost-effectiveness analysis affirms that incorporating TTFields into TMZ treatment proves to be cost-effective, given a threshold three times the Chinese per capita GDP.

Revisiting the relationships between energy consumption, economic development and urban size: A global perspective using remote sensing data

Existing methods of measuring energy consumption require complex statistics and computing. A real-time and globally applicable approach for comparing energy consumption across different cities is still lacking. Additionally, the nonlinear relationships and varying thresholds of energy consumption in relation to economic activities and urbanization remain unconfirmed. This study aims to fill these gaps by utilizing Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data in 2015 and a top-down approach based on a multiple regression model to examine energy consumption in global cities employing a redefined urban boundary. It also explores the accurate relationship between energy consumption, population density (as a proxy of urbanization), and per capita gross domestic product (GDP) across different regions and urban sizes using generalized additive models and regression models. High-resolution gridded population and GDP datasets covering the entire planet are utilized for this purpose. The study also estimates the development potentiality. The study yields followings outcomes: Firstly, the top 30 cities with the highest per capita energy consumption account for over 0.66% of the total per capita energy consumption of all cities. Secondly, in East Asia (EA) and Southeast Asia (SEA), the per capita energy consumption decreases when per capita GDP reaches $40,000 and $75,000, respectively, while it remains stable in cities located in Western Europe (WE) and North America (NA) as per capita GDP increases. Thirdly, the per capita energy consumption declines with increasing urban population density until reaching 10,000 person/km2, 22,000 person/km2, and 4000 person/km2 in EA, SEA, and NA, respectively. Fourthly, in Central Asia (CA), megacities can save over 100 Mbtu/population when per capita GDP increases by $1000 compared to big cities. This pioneering study provides a comparable investigation of energy consumption at the global city level, exploring its relationship with urbanization and economy by employing a unified calculation standard. It will facilitate long-term energy-saving policies and urban planning strategies.

Cost-effectiveness analysis of emicizumab prophylaxis in patients with haemophilia A in India.

Emicizumab is the initial subcutaneously administered bispecific antibody approved as a prophylactic treatment for patients with haemophilia A (PwHA). This study assessed the economic evaluation of emicizumab treatment for non-inhibitor severe haemophilia A (HA) patients in India. A Markov model evaluated the cost-effectiveness of emicizumab prophylaxis compared to on-demand therapy (ODT), low-dose prophylaxis (LDP; 1565IU/kg/year), intermediate-dose prophylaxis (IDP; 3915IU/kg/year) and high-dose prophylaxis (HDP; 7125IU/kg/year) for HA patients without factor VIII inhibitors. Inputs from HAVEN-1 and HAVEN-3 trials included transition probabilities of different bleeding types. Costs and benefits were discounted at a 3.5% annual rate. In the base-case analysis, emicizumab was cost-effective compared to HDP, with an incremental cost-effectiveness ratio (ICER) per quality-adjusted life-years (QALY) of Indian rupees (INR) 27,869. Compared to IDP, ODT and LDP, emicizumab prophylaxis could be considered a cost-effective option if the paying threshold is >1 per capita gross domestic product (GDP) with ICER/QALY values of INR 264,592, INR 255,876 and INR 305,398, respectively. One-way sensitivity analysis (OWSA) highlighted emicizumab cost as the parameter with the greatest impact on ICERs. Probabilistic sensitivity analysis (PSA) indicated that emicizumab had a 94.7% and 49.4% probability of being cost-effective at willingness-to-pay (WTP) thresholds of three and two-times per capita GDP. Emicizumab prophylaxis is cost-effective compared to HDP and provides value for money compared to ODT, IDP, and LDP for severe non-inhibitor PwHA in India. Its long-term humanistic, clinical and economic benefits outweigh alternative options, making it a valuable choice in resource-constrained settings.

FDI and Growth: A Re-look at Evidence on Developing Economies

Using cross-country data, this article investigates the impact of foreign direct investment (FDI) on growth in developing economies during globalisation. The study adopts an approach different from existing studies by categorising countries from Asia, Africa, Latin America, and the Caribbean based on FDI growth and per capita gross domestic product (GDP) growth relationship in the two phases: 1991–2004 and 2005–2019. In a sample of 29 countries, the least-squares dummy variable (LSDV) estimation shows a significant negative between FDI and per capita GDP controlling for both country-specific and time-specific effects and various other factors including gross domestic capital formation, inflation, physical infrastructure, human capital, trade openness, financial development and governance. This study confirms the positive impact of FDI in the earlier phase, while a significant negative impact on the later phase. The observed negative relationship holds for sub-sets of the sample countries for the entire period. These results are despite the importance of human capital, infrastructure, financial development, trade openness and governance along with FDI. These developing countries thus need to formulate complementary domestic economic policies, including institutional changes, which complement foreign direct investment in promoting per capita growth. JEL: F21, F43, O11, C23

Economy and carbon emissions optimization of different provinces or regions in China using an improved temporal attention mechanism based on gate recurrent unit

With the implementation of 14th Five-Year Plan in China and the completion of the poverty alleviation task, the economy in China has made great progress. However, the carbon dioxide (CO2) emission has also increased, which will lead to the greenhouse effect for threatening economic development and affecting people's lives. Global warming has become the greatest challenge to contemporary economic and social development, and the transition to low-carbon economy has become a general trend of world economic development. Meanwhile, the traditional neural network method for predicting the economy and CO2 emissions is not accurate and objective because of the unbalanced labeling of training data and the inability to capture the relationship between time series data. Therefore, an economy and CO2 emission prediction model based on temporal attention Gate Recurrent Unit (GRU) (TA-GRU) with loss function Balanced Mean Square Error (BMSE) (BTA-GRU) mechanism is proposed to analyze and optimize the energy structure of 27 provincial-level administrative regions in China. Due to the unbalanced data labels in the different energy structure of different regions, the improved loss function BMSE is used to solve the problem of unbalanced labels from the perspective of statistics. Then, with coal, gasoline, petroleum, coke, fuel oil, diesel, natural gas and crude oil as inputs, the per capita Gross Domestic Product (GDP) as the desired output and the CO2 as the undesired output, the GRU can better capture time step dependencies in time series, and the attention mechanism can assign different weights to each time step, so that the proposed method can better learn data features and improve the accuracy of the an economic and CO2 emission prediction model. Finally, the CO2 emission and economic prediction model of different provincial-level administrative regions in China is established based on this proposed method. The experimental results show that compared with other mainstream time series data prediction models, this proposed model has better stability and practicability, and its mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) reach 3.43, 3.28% and 2449.57, respectively. Furthermore, according to the actual situation of efficient provincial administrative regions, the energy structure of inefficient provincial administrative regions can be adjusted and optimized. Such as the energy structure of Hebei Province can be adjusted according to the energy structure of Beijing, it is estimated that the CO2 emissions of Hebei will be reduced by 82,972.43 ×10000 tons (WT) and its per capita GDP will be increased by 115,856 Yuan. Moreover, Chinese provincial governments should vigorously promote the use of clean energy, accelerate the establishment of a sound green low-carbon circular development economic system, and help achieve the goal of carbon neutrality.

Progression of an emission inventory of China integrating CO2 with air pollutants: A chance to learn the influence of development on emissions

An emission inventory, involving both air pollutants (APs) and greenhouse gases (GHGs), is needed so that the questions of air pollution and climate change can be co-addressed, coordinated, and collaborated upon. In this study, an emission inventory, including both APs and CO2 (representing GHGs), was developed for China 2019 using two special measures. First, the framework of air pollution source classification was used for the integrative inventory and then the CO2 emissions for each sector/subsector/source in the GHG inventory were precisely arranged into the framework of APs by every possible means such as drawing on the constraints from the International Industrial Classification of All Economic Activities (ISIC), which enabled the sharing of common or intrinsically connected activity data for both APs and CO2. Second, an attempt was made to deduce the activity data of various industrial processes through proper interpretation of the information carried in Pollutant Permits. The calculated emissions of APs and CO2 were found to be comparable to the results of other studies. Additionally, the average per capita CO2 and pollution equivalents (PEs) were 10.29 ± 6.75 tons and 11.72 ± 7.35 tons, respectively, over the provincial regions with per capita gross domestic product (GDP) lower than ￥70000 (about $10000). Whereas the average per capita CO2 and PEs dropped to 6.10 ± 1.83 tons and 6.70 ± 2.09 tons, respectively, over the provincial regions with per capita GDP higher than ￥70000. This somewhat supports the philosophy of the Environmental Kuznets Curve (EKC) regarding the close connection between emission and development. Moreover, the two emission intensities (for APs and CO2) were found to vary synchronously among the provinces, reflecting the inter-linked fates of the two emissions under the control measures oriented toward either APs or GHGs.

Examining the Impact of Hysteresis on the Projected Adoption of Autonomous Vehicles

This study explores the potential impact of per capita gross domestic product (GDP) changes on the adoption of autonomous vehicles (AVs). The level of adoption of AVs is anticipated to influence the benefits of future mobility, prompting numerous studies that forecast the market share of AVs using various methods. The influence of changes in the per capita GDP on vehicle ownership is crucial in assessing the challenges associated with reducing dependence on AVs in the future. This phenomenon, known as the hysteresis effect, implies that AV adoption estimates may differ when the GDP is rising as opposed to when it is falling. This research examines the effect of rising and falling GDP per capita on the anticipated AV diffusion in Hungary, utilising a scenario-based method to account for the variation in adoption rates in the literature. The study findings indicate that declines in GDP in the past will impact AV ownership, leading to a shift in future adoption patterns. The AV market is projected to reach saturation in the 2070s and the 2090s in favourable and moderate scenarios, respectively, while a pessimistic state would delay this outcome until after the year 2100.

China's Coal Ban policy: Clearing skies, challenging growth

China's Coal Ban policy is one of the world's most extensive and ambitious programs designed to mitigate air pollution. However, the effects of this policy on the environment and the economy remain unknown. This study examines the impacts of the Coal Ban policy, which has been implemented in 28 cities in Beijing and its adjacent provinces, on air quality and economic growth. Based on a panel dataset spanning 138 cities between 2010 and 2019, the policy was found to have reduced atmospheric particulate matter with a diameter less than or equal to 2.5 μm (PM2.5) by 4.74 μg/m3 in the 28 cities, but have also reduced per capita gross domestic product (GDP) by 5.8%. Further, the policy has also produced spatial spillover effects. In cities near the 28 cities, the policy has reduced PM2.5 by 4.40 μg/m3 and per capita GDP by 0.9%. Robustness tests corroborated the reliability of the conclusions. These findings underscore the importance of fostering a harmonious relationship between efforts to mitigate air pollution and the pursuit of economic growth objectives.

Spatial autocorrelation and related factors of stroke mortality in Zhejiang Province based on spatial panel model in 2015-2020

Objective: To explore the spatial autocorrelation and macro influencing factors of stroke mortality in Zhejiang Province in 2015-2020 and provide a scientific basis for stroke prevention and control strategy. Methods: The data on stroke death were obtained from Zhejiang Chronic Disease Surveillance System. The spatial distribution of stroke mortality was explored by mapping and spatial autocorrelation analysis. The spatial panel model analyzed the correlation between stroke mortality and socioeconomic and healthcare factors. Results: From 2015 to 2020, the average stroke mortality was 68.38/100 thousand. The standard mortality of stroke was high in the areas of east and low in the west, high in the south and low in the north. Moreover, positive spatial autocorrelation was observed (Moran's I=0.274-0.390, P<0.001). Standard mortality of stroke was negatively associated with per capita gross domestic product (GDP) (β=-0.370, P<0.001), per capita health expenditure (β=-0.116, P=0.021), number of beds per thousand population (β=-0.161, P=0.030). Standard mortality of ischemic stroke was negatively associated with per capita GDP (β=-0.310, P=0.002) and standard management rate of hypertension (β=-0.462, P=0.011). Standard mortality of hemorrhagic stroke was negatively associated with per capita GDP (β=-0.481, P<0.001), per capita health expenditure (β=-0.184, P=0.001), number of beds per thousand population (β=-0.288, P=0.001) and standard management rate of hypertension (β=-0.336, P=0.029). Conclusions: A positive spatial correlation existed between stroke mortality in Zhejiang Province in 2015-2020. We must focus more on preventing and controlling strokes in relatively backward economic areas. Moreover, to reduce the mortality of stroke, increasing the investment of government medical and health funds, optimizing the allocation of medical resources, and improving the standard management rate of hypertension are important measures.

Willingness to pay for cancer prevention versus treatment in China: implications for cost-effectiveness threshold

ABSTRACT Background Empirical support for the appropriate cost-effectiveness threshold (CET) in China remains sparse. Objective This study aimed to estimate the willingness to pay (WTP) for cancer prevention and treatment from the perspective of healthcare policy-makers (i.e. supply side) and to investigate whether there is a difference between the estimated WTP in two scenarios. Methods We conducted a web-based survey from May to July 2022 among experts who offering consultation to the government. We surveyed 79 experts from a national think-tank (84.81% response rate) using contingent valuation method, a method for estimating the monetary value that individuals place on a non-market service. Results The mean WTP for two scenarios were estimated at 1.29 times of per capita Gross Domestic Product (GDP) of China and 1.90 times of per capita GDP, respectively. There was a difference between the WTP in the two scenarios and the WTP for treatment was significantly higher than prevention. Conclusion The findings suggest that though there is a smaller gap between the two scenarios in China as compared to other countries, the WTP may vary under different scenarios. So there’s a need to further refine the development of CET by adding parameters like prevention instead of defining one universal threshold.

Association between uveitis onset and economic development in mainland China

BackgroundEconomic prosperity has fostered numerous changes that may translate into better or worse outcomes across all domains of health. This study aims to explore the associations of economic development with uveitis onset in mainland China.MethodsWe used Poisson regression with generalized estimated equations to quantify the associations of per capita gross domestic product (GDP) with uveitis onset in 31 provinces of mainland China from 2006 to 2017. We further estimated the effects mediated by economic growth on the temperature-uveitis and PM2.5-uveitis associations established in our previous studies.ResultsA total of 12,721 uveitis patients from 31 provinces of mainland China were studied. Overall, every 10,000 Chinese yuan ($ 1491.278, 2006–2017) increase in per capita GDP, with no weighted value or weighted by population, corresponded to 1.85% (95% confidence interval, 1.19–2.52%) and 1.43% (95% confidence interval, 0.37–2.51%) lnRR decrease in the uveitis onsets. Stratified analysis showed this negative association between per capita GDP and uveitis onset, only existed in male patients (P < .001), individuals aged 20–50 years (P < 0 .05), non-infectious uveitis, uveitis with systemic disease, and Bechet’s disease (all P < 0 .05). Moreover, the increased per capita GDP, if above the national level, could reinforce both temperature-uveitis and PM2.5-uveitis association (both P < 0.001).ConclusionsThe findings suggest that economic development is negatively associated with uveitis onset. However, it may facilitate the uveitis onset mediated by both increased temperature and PM2.5 exposure if the per capita GDP is above national level.

Macroeconomic determinants of stock market returns: Evidence from Oman

This paper aims to analyze the long- and short-term effects of macroeconomic factors on stock market returns in the Sultanate of Oman, particularly the effects of inflation (CPI), interest rates (IR), foreign exchange reserve (FER), money supply (M2), per capita gross domestic product (GDP), trading balance (TB), and oil prices (OP) on the returns of the main index of the Muscat Stock Exchange (MSX30). Additionally, the paper aims to examine whether the effects changed after the low oil price crisis at the end of 2014. The autoregressive distributed lag (ARDL) model was employed to analyze quarterly data for the full sample period (2009 Q2–2022 Q4), and for the oil price crisis period (2014 Q4–2022 Q4). The findings indicate that FER and per capita GDP had positive long-term effects on the returns of the MSX30, while M2 had a negative effect. It was also found that FER and OP had a negative effect in the short run, and the short-term impact of IR and TB were positive. Moreover, the findings during the oil price crisis indicate that the M2 continued to have a negative long-term impact, while in the short run, FER had a positive impact and TB had a negative impact.

MSME Promotion, Inequality and Decentralized Wealth Creation: A Strategic Policy Perspective

The study aims to ascertain the impact of micro, small and medium enterprises (MSMEs) growth on wealth creation and its distribution in India concerning macroeconomic variables such as GDP (Gross Domestic Product) per capita, unemployment, Foreign direct investment (FDI), human development index (HDI) and the urban population (URP). The research has examined the underlying short-term association using ECM, FMOLS, DOLS and CCR to estimate the long-run association between the identified variables. It documents a positive relationship between the share of the B50 populace and the FDI. On the other hand, per capita GDP, in the long run, had a negative association with MSME share, URP and HDI. Similarly, for the top 10 percentile (T10) share, there was a positive relationship between MSME share and URP. Therefore, the results document a decentralized wealth creation for an MSME-focused development strategy in the short run to address skewed wealth creation in emerging societies.

Comparative Study on the Influencing Factors of the Greenhouse Gas Budget in Typical Cities: Case Studies of Beijing and Shenzhen

Clarifying the pattern of the urban greenhouse gas (GHG) budget and its influencing factors is the basis of promoting urban low-carbon development. This paper takes Beijing and Shenzhen—the capital city and the most rapidly developing city in China, respectively—as case studies, comprehensively accounts their GHG budgets from 2005 to 2020, and investigates and compares the factors affecting their GHG budgets. The total GHG emissions in Beijing were lowest in 2005 (160.3 TgCO2 equivalents) and peaked at 227.7 TgCO2 equivalents in 2019, and then decreased to 209.1 TgCO2 equivalents in 2020. Meanwhile, the total GHG emissions in Shenzhen gradually increased from 36.0 TgCO2 equivalents in 2005 to 121.4 TgCO2 equivalents in 2019, and then decreased to 119.1 TgCO2 equivalents in 2020. The energy activity sector was the greatest contributor to GHG emissions in this period, accounting for 82.5% and 76.0% of the total GHG emissions in Beijing and Shenzhen, respectively. The carbon sink of the ecosystems of these two cities could absorb only small parts of their emissions, and the neutralization rates of sinks ranged from 1.7% to 2.3% in Beijing and from 0.3% to 1.5% in Shenzhen. The enhancement of population, economic product, and consumption increased the greenhouse gas emissions in both cities. A 1% increase in population size, per capita GD (gross domestic product), and residential consumption level would increase total GHG emissions by 0.181%, 0.019%, and 0.030% in Beijing, respectively. The corresponding increases in Shenzhen would be 0.180%, 0.243%, and 0.172%, respectively. The household size had opposite effects on the two cities, i.e., a 1% increase in household size would increase GHG emissions by 0.487% in Shenzhen but reduce them by 2.083% in Beijing. Each 1% increase in secondary industry and energy intensity would reduce GHG emissions by 0.553% and 0.110% in Shenzhen, respectively, which are more significant reductions than those in Beijing.

A Narrative of Urban Underground Pipeline System Disasters in China in 2021: Spatial and Temporal Distribution, Causal Analysis, and Response Strategies

In recent years, there have been frequent disasters and accidents in the underground pipeline system of Chinese cities, posing a continuous threat to the safety of life and property of the public and the order of urban operations. This article uses statistical data on the spatiotemporal distribution of major underground pipeline disasters in various provinces of China in 2021. By using Pearson’s correlation coefficient and regression analysis, a causal analysis model for urban underground pipeline accidents is constructed, and the correlation between urban underground pipeline accident rate and economic and social indicators is analyzed. From the analysis, it has been found that the correlation coefficients of gross domestic product (GDP) per capita, the pipeline density in the built-up area, and the urban pipeline accident rate reach −0.4019 and −0.4275, respectively, showing a negative correlation. Further, the regression analysis results show that the underground pipeline accident rate shows a decreasing trend as the per capita GDP and the density of pipelines in the built-up areas increase. Among them, the results of the power function fitting model show that the GDP per capita accounts for 48.10% of the urban underground pipeline accidents, whereas the pipeline density of the built-up area accounts for 58.27% of these accidents. The construction of underground pipeline regulations is influential in reducing underground pipeline disaster accidents. In this study, the role and effectiveness of the factors, such as the construction of professional regulations in maintaining the safe operation of urban underground pipelines, has been discussed, and suggestions and methods to improve the problems that need to be solved, such as the construction of a municipal supporting emergency management system in the safe operation of underground pipelines in the future, have been proposed.

Spillover effects of urbanization on carbon emissions: A global view from 2000 to 2019

An evident increase in carbon emissions (CEs) mainly caused by urbanization has aroused extensive concern. Insufficient research exists on the global impact of urbanization on CEs and their spillover effects. This study addresses this gap by investigating the spillover effects of urbanization on CEs. It employs bivariate spatial autocorrelation analysis and spatial econometric models to analyze data for 125 countries from 2000 to 2019. The results showed that developed and major oil-exporting countries had higher per capita CEs than other countries; however, with a downward trend. The bivariate Moran's I index between urbanization level and per capita CEs reached a maximum of 0.403 in 2008 and then continuously decreased to 0.343 in 2019, to a certain extent, which indicates that the promoting effect of urbanization on CEs was weakening. The results of spatial econometric models presented an inverted U-shaped curve between global urbanization and per capita CEs, with a threshold of 68%. Furthermore, the findings revealed that an increase in proportion of added value of secondary and tertiary industries to gross domestic product (GDP), as well as an increase in per capita GDP, tended to be associated with higher per capita CEs. Conversely, an increase in population density may produce a decline in per capita CEs. Our study highlights the global impact of urbanization on CEs and underscores the importance of developing policies for sustainable urbanization and carbon emission reduction globally.

An analysis of India’s export potential in South Asia under SAFTA

There is a consensus in the literature that higher exports are associated with increased economic growth, productivity, and employment. This paper attempts to analyze India’s export potential in the South Asian region following the South Asian Free Trade Area (SAFTA) agreement. Using panel data, an augmented gravity model analysis is employed for the period from 2004 to 2021. The analysis finds a positive and significant effect of trading countries’ gross domestic product (GDP) on India’s total exports, whereas the effect of the per capita GDP differential between the countries is negative. This paper does not find any significant effect of distance, border, or common language on India’s total exports. The study also finds that from 2004 to 2021, India’s average export potential was positive with three countries (Pakistan, Bangladesh, and the Maldives) and negative with four countries (Nepal, Sri Lanka, Afghanistan, and Bhutan). India had the highest export potential with Pakistan during this period. The study suggests that India must raise its exports to those South Asian Association for Regional Cooperation (SAARC) member nations where it has not been able to export in accordance with its potential and explore trade opportunities with other countries in Asia, Europe, and North America. In this context, it is crucial to focus on trade and infrastructural reforms to boost India’s exports.

Air cargo transport demand forecasting using ConvLSTM2D, an artificial neural network architecture approach

The prediction of air traffic demand (passengers and cargo) in a regional/national air transport system is essential. Knowing the behavior of future demand helps, on the one hand, the design and execution of air transport public policies, which, for example, help to focus, guide and prioritize investment (public and private) for the expansion / modernization of airport infrastructures (or development of new airports), act on tariff policies, implement changes in regulatory policy, etc.; on the other hand, it helps airport managers to plan the airport. Therefore, in this paper, a short-term forecast (5 years) of the demand for air cargo transport was carried out, applied to a specific case study (Colombia), taking into account the most severe pandemic period (the year 2020). To perform the forecast, an approach based on Machine Learning/Deep Learning (ML/DL) method comprising a hybrid of convolutional and recurrent memory neural networks (that allow space-temporal non-linear analysis, such as multi-variable spaces and temporal multi-steps), is presented. The analysis developed here establishes the optimal length of the prediction period; on the other hand, the proposed methodology allows the identification of the most relevant socioeconomic features in the prediction of air cargo demand (domestic and international), i.e., interpreting the ML/DL results obtained through the variational analysis of different combinations of features. The results show that international air cargo demand is strongly dependent on Gross Domestic Product (GDP) and PCG (Per Capita GDP), while domestic air cargo demand is significantly dependent on PCG. Finally, the results show, for the case study country, very rapid recovery of air cargo demand at pre-pandemic rates (behavior already found in other recent studies and research).