Gross Domestic Product Density Research Articles

Mapping China’s Changing Gross Domestic Product Distribution Using Remotely Sensed and Point-of-Interest Data with Geographical Random Forest Model

Accurate knowledge of the spatiotemporal distribution of gross domestic product (GDP) is critical for achieving sustainable development goals (SDGs). However, there are rarely continuous multitemporal gridded GDP datasets for China in small geographies, and less is known about the variable importance of GDP mapping. Based on remotely sensed and point-of-interest (POI) data, a geographical random forest model was employed to map China’s multitemporal GDP distribution from 2010 to 2020 and to explore the regional differences in the importance of auxiliary variables to GDP modeling. Our new GDP density maps showed that the areas with a GDP density higher than 0.1 million CNY/km2 account for half of China, mainly distributed on the southeast side of the Hu-line. The proportion of the areas with GDP density lower than 0.05 million CNY/km2 has decreased by 11.38% over the past decade and the areas with an increase of 0.01 million CNY/km2 account for 70.73% of China. Our maps also showed that the GDP density of most nonurban areas in northeast China declined, especially during 2015–2020, and the barycenter of China’s GDP moved 128.80 km to the southwest. These results indicate China’s achievements in alleviating poverty and the widening gaps between the South and the North. Meanwhile, the number of counties with the highest importance score for POI density, population density, and nighttime lights in GDP mapping accounts for 52.76%, 23.66%, and 23.56%, respectively, which suggests that they play a crucial role in GDP mapping. Moreover, the relationship between GDP and auxiliary variables displayed obvious regional differences. Our results provide a reference for the formulation of a sustainable development strategy.

Climate change and human activity impacts on future flood risk in the Pearl River Delta based on the MaxEnt model

In the background of global climate change and rapid urbanization, extreme climate events are frequent, and highly urbanized areas flooding problems are becoming increasingly prominent. It becomes important to develop qualitative scenario storylines to assess future flooding risk in a changing environment over the highly urbanized areas. In this study, the future (2030–2050) flood risk in the Pearl River Delta (PRD) region of China was assessed based on the MaxEnt model. We have developed four future scenarios under different emission conditions based on the representative concentration pathways (RCPs) and the shared socio-economic pathways (SSPs). The MaxEnt model was trained by using flood hazard sample point data and flood risk indicators, meanwhile, food risk prediction with high accuracy was obtained. We analyzed the influencing factors of flood risk and predicted the flood risk of the PRD under four future scenarios. According to the results, there is a significant increase in the size and proportion of high flood risk areas in most scenarios. Flood risk under scenario SSP5-RCP8.5 are expected to be the most serious in the future. Population density (POPD) and Gross domestic product density (GDPD) have the highest explanatory power for flood risk. This study predict the changes of flood risk under the combined influence of climate change and human activities, and hoped to provide a reference for future planning and disaster mitigation construction in PRD cities.

Net Primary Productivity Variations Associated with Climate Change and Human Activities in Nanjing Metropolitan Area of China.

Rapid economic development has changed land use and population density, which in turn affects the stability and carbon sequestration capacity of regional ecosystems. Net primary productivity (NPP) can reflect the carbon sequestration capacity of ecosystems and is affected by both climate change and human activities. Therefore, quantifying the relative contributions of climate change and human activities on NPP can help us understand the impact of climate change and human activities on the carbon sequestration capacity of ecosystems. At present, researchers have paid more attention to the impact of climate change and land use change on NPP. However, few studies have analyzed the response of the NPP to gross domestic product (GDP) and population density variations on a pixel scale. Therefore, this paper analyzes the impact of climate change and human activities to NPP on a pixel scale in the Nanjing metropolitan area. During the period 2000-2019, the annual mean NPP was 494.89 g C·m-2·year-1, and the NPP in the south of the Nanjing metropolitan area was higher than that in the north. The NPP was higher in the forest, followed by unused land, grassland, and cropland. In the past 20 years, the annual mean NPP showed a significant upward trend, with a growth rate of 3.78 g C·m-2·year-1. The increase in temperature and precipitation has led to an increasing trend of regional NPP, and the impact of precipitation on NPP was more significant than that of temperature. The transformation of land use from low-NPP type to high-NPP type also led to an increase in NPP. Land use change from high-NPP type to low-NPP type was the main cause of regional NPP decline. Residual analysis was used to analyze the impact of human activities on NPP. Over the last 20 years, the NPP affected by human activities (NPPhum) showed a high spatial pattern in the south and a low spatial pattern in the north, and the annual mean NPPhum also showed a fluctuating upward trend, with a growth rate of 2.00 g C·m-2·year-1. The NPPhum was influenced by both GDP and population density, and the impact of population density on NPP was greater than that of GDP.

Investigating the asymmetry effects of crude oil price on renewable energy consumption in the United States.

The reduction in oil prices might make crude oil a cheaper alternative to renewable energy (RE). Given this, the present paper examines the effect of fluctuation of oil prices on the use of RE in the United States (US) during the period 1970 to 2018. We constructed two nonlinear autoregressive distributed lag (NARDL) models to examine the effect of the positive and negative oil price shocks on the use of RE in the US. The RE consumption is taken as the dependent variable and the gross domestic product (GDP), Brent crude prices, population density, trade openness, and price index as independent variables. The result revealed that the rise in crude oil price, GDP, and population density will increase RE use in the short run and in the long run as well. Moreover, the study finds that any decrease in oil prices will decrease RE use in the short run and its effect will eventually diminish in the long run. On the policy front, it is suggested that US should raise its energy security by reducing its dependency on imported crude oil and increase the role of RE through the imposition of taxes on oil and increase the base of production and consumption through a series of measures.

Effect of Urbanization on Ecosystem Service Values in the Beijing-Tianjin-Hebei Urban Agglomeration of China from 2000 to 2014

Rapid urbanization has led to the continuous deterioration of the surrounding natural ecosystem. It is important to identify the key urbanization factors that affect ecosystem services and analyze the potential effects of these factors on the ecosystem. We selected the Beijing, Tianjin, and Hebei (BTH) urban agglomeration to investigate these effects, and designed three indicators to map the urbanization level: Population density, gross domestic product (GDP) density, and the construction land proportion. Four indicators were chosen to quantify ecosystem services: Food production, carbon sequestration and oxygen production, water conservation, and soil conservation. To handle the nonlinear interactions, we used a random forest (RF) method to assess the effect of urbanization on ecosystem services in the BTH area from 2000 to 2014. Our study demonstrated that population density and economic growth were the internal driving forces affecting ecosystem services. We observed changing trends in the effect of urbanization: The effect of population density on ecosystem services increased, the effect of the proportion of construction land was consistent with population density, and the effect of GDP density on ecosystem services decreased. Our results suggest that controlling the population and GDP would significantly influence the sustainable development in large urban areas.

Detecting influences of factors on GDP density differentiation of rural poverty changes

We use geographical detectors to quantify the interactive influence of impact factors on gross domestic product (GDP) density changes on rural poverty, for identifying the differentiation mechanism in rural poverty. The optimal characteristics of the main factors that benefit GDP density growth were determined. We proposed poverty alleviation policies and measures for different types of poverty-stricken regions. Distance to main roads, the normalised differential vegetation index (NDVI), land use, average annual temperature, and elevation can satisfactorily account for GDP density changes in rural poverty. Impact factors have an interactive influence on GDP density. The synergistic effect of impact factors manifests itself as mutual enhancement and nonlinear enhancement, and the interaction of two impact factors strengthens the influence of each individual factor. We revealed the poverty differentiation mechanisms of economic development in poverty villages,and put forward targeted poverty alleviation measures with stratified guidance and key breakthroughs.

Spatiotemporal Evolution Pattern and Driving Factors of Higher-Education Population in China

The spatial and temporal distribution of the higher-education population (HEP) is a fundamental characteristic of the development level of higher education in a region or a country. Based on the annual population sampling statistics from 2000 to 2015, the spatiotemporal evolution pattern of the HEP in China is systematically analyzed. Meanwhile, 9 driving factors related to natural conditions and socioeconomic conditions of average slope, average elevation, the city location, the city size, high-speed railways, highways, gross domestic product (GDP) density, nonagricultural population, and population density of 2000 and 2010 at the municipal level are constructed. Then, the factors driving the distribution of the HEP are quantitatively analyzed using the geodetector model. The results show that the centroid of the HEP, shifting from the northeast to the southwest from 2000 to 2010, is markedly different from that of the total population from 2000 to 2015 in China. Despite their different moving directions, the distance between the two centroids is decreasing, indicating both significant regional differences of the HEP in China and a narrowing gap between the HEP and the total population in recent years. The results of the factor detector of 2000 and 2010 suggest that the proportion of the nonagricultural population and the city location are the main driving factors of the distribution of the HEP, with driving forces between 0.494 and 0.627, followed by the city size, highways, and GDP density, with driving forces are between 0.199 and 0.302. It indicates that urbanization levels and urban locations are the main factors affecting the spatial distribution of the HEP. The results of the interaction detection reveal that the interaction of the nonagricultural population and the GDP density can explain 92.7% of the spatial variety of the HEP in 2000, while that of the nonagricultural population and the population density can explain 97.6% of the spatial variety of the HEP in 2010, which reflects a more balanced development of the HEP. In addition, a large proportion of the HEP transfers from economically developed areas to densely populated areas.

Coupled effects of urbanization level and dam on microplastics in surface waters in a coastal watershed of Southeast China

Investigating the heterogeneous distribution pattern of plastic pollution in river systems is useful for better understanding the transport pathways of plastics from land to sea. Therefore, we analyzed samples from 17 sites to evaluate the distribution pattern of microplastics (MPs) in Minjiang River Watershed. The MP concentrations ranged between 0.12 and 2.72 n/L, with higher microplastic concentrations located in urban areas with greater population and gross domestic product (GDP) densities. MP concentrations were positively correlated with population density, GDP density, and built-up land use percentage, suggesting that increasing urbanization level will lead to greater plastic pollution. MP concentrations spanning the upstream and downstream sides of the SK Hydropower Dam showed decreasing trends, suggesting that MP settling would occur behind the dam and Shuikou Reservoir might serve as a sink for MPs. This study provides useful insight for revealing the effects of urbanization and dams on spatial distribution of riverine MPs.

GDP SPATIALIZATION AND DYNAMIC ANALYSIS BASED ON DMSP/OLS IMAGES: A CASE OF HENAN PROVINCE

Abstract. Gross Domestic Product (GDP) is an important statistical indicator for quantitatively studying of economic development and space distribution characteristics. However, GDP has been often calculated based on administrative units without fine describing the difference of space distribution in the administrative unit. This paper calculated night-light indexes of Henan province using DMSP/OLS remote sensing data in 2006 and 2013. Those correlation between different light indexes and GDP was analyzed, and the best regression model of GDP was constructed. The accuracy of statistical data in 2006 and 2013 was used to verify the accuracy. The spatial visualization and dynamic analysis of GDP in Henan province were realized. Finally, this paper realized the spatial visualization and dynamic analysis of GDP in Henan province and produced 1 km × 1 km distribution map of GDP density.The results showed that the Total Night-time Light (TNL) index had the highest correlation with GDP in 2010 with the coefficient of 0.928, R2 was 0.861. The TNL indicator and GDP in 2013 achieved the correlation coefficient of 0.930, R2 was 0.865. the average relative error between GDP simulation value and statistical value in 2006 and 2013 is 8.19%, 8.08%, respectively.The GDP density map of 18 prefecture-level cities in Henan province showed an obvious expansion trend from 2006 to 2013.This research demonstrated that the Night-time light data could be used as an important index of analyzing economic status of Henan province. The model between light indicators and GDP could better simulate the spatial distribution of regional economic development.

Forecasting Forest Areas in Myanmar Based on Socioeconomic Factors

National circumstances should be considered in establishing and adjusting forest reference emission levels (FRELs/FRLs) under the United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation (UN-REDD+ Programme). Myanmar, one of the world’s least developed countries may face accelerating deforestation under an open and democratic political system that desires rapid economic development. This research analyzes the impacts of population growth and economic development on forest areas in Myanmar by using panel data analysis, an econometrics approach based on panel data of forest areas, population, and gross domestic product (GDP) by states and regions in 2005, 2010, and 2015. This research revealed that per capita GDP and population density gave statistically significant negative impacts on forest areas. Using the regression model obtained above, medium population growth projections, and three GDP development scenarios, annual forest areas from 2016 to 2020 were forecast. The forecasting results showed possible higher deforestation under higher economic development. Finally, this research showed the necessity of adjusting the current average deforestation for RELs in the REDD+ scheme in Myanmar and the direction in which the adjustment should go.

GDP spatialization in Ningbo City based on NPP/VIIRS night-time light and auxiliary data using random forest regression

Accurate spatial distribution information on gross domestic product (GDP) is of great importance for the analysis of economic development, industrial distribution and urbanization processes. Traditional administrative unit-based GDP statistics cannot depict the detailed spatial differences in GDP within each administrative unit. This paper presents a study of GDP spatialization in Ningbo City, China based on National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL) data and town-level GDP statistical data. The Landsat image, land cover, road network and topographic data were also employed as auxiliary data to derive independent variables for GDP modelling. Multivariate linear regression (MLR) and random forest (RF) regression were used to estimate GDP at the town scale and were assessed by cross-validation. The results show that the RF model achieved significantly higher accuracy, with a mean absolute error (MAE) of 109.46 million China Yuan (CNY)·km−2 and a determinate coefficient (R2 = 0.77) than the MLR model (MAE = 161.8 million CNY·km−2, R2 = 0.59). Meanwhile, by comparing with the estimated GDP data at the county level, the town-level estimated data showed a better performance in mapping GDP distribution (MAE decreased from 115.1 million CNY·km−2 to 74.8 million CNY·km−2). Among all of the independent variables, NTL, land surface temperature (Ts) and plot ratio (PR) showed higher impacts on the GDP estimation accuracy than the other variables. The GDP density map generated by the RF model depicted the detailed spatial distribution of the economy in Ningbo City. By interpreting the spatial distribution of the GDP, we found that the GDP of Ningbo was high in the northeast and low in the southwest and formed continuous clusters in the north. In addition, the GDP of Ningbo also gradually decreased from the urban centre to its surrounding areas. The produced GDP map provides a good reference for the future urban planning and socio-economic development strategies.

THE EFFECT OF GROSS DOMESTIC PRODUCT AND POPULATION GROWTH ON CO2 EMISSIONS IN INDONESIA: AN APPLICATION OF THE ANT COLONY OPTIMISATION ALGORITHM AND COBB-DOUGLAS MODEL

Gross Domestic Product (GDP) is one indicator for measuring a country's economic growth. However, the increase in GDP and population growth are affecting CO2 emissions. This study analyses the effects of GDP and population density on CO2 emissions in Indonesia. To this end, it used the Cobb-Douglas model, and parameter estimation using Ant Colony Optimisation algorithm. The analysis of the results reveals that GDP and population density influence CO2 emissions in Indonesia significantly, and significantly follows the Cobb-Douglas model with increasing return to scale characteristics. Thus, an increase in GDP and population density will lead to increased CO2 emissions in Indonesia.Keywords: Economic Growth, Gross Domestic Product, Population Growth, CO2 Emission, Ant Colony Optimisation Algorithm, Cobb-Douglas modelJEL Classifications: C61, O47, O150, Q530DOI: https://doi.org/10.32479/ijeep.8011

Spatial Accessibility of Urban Forests in the Pearl River Delta (PRD), China

The Pearl River Delta (PRD) is one of the most important economic zones both in China and in the world. Its rapid economic development has been associated with many environmental problems such as the loss of forests in urban areas. We estimated the accessibility of forests in the PRD by quantifying spatial proximity and travel time. We found that distances from a large proportion of the points of interest (POIs) (~45%) and urban lands (~38%, where ~49 urban residents live) to the nearest forests were greater than 1000 m; suggesting a low spatial proximity to forests. Urban parks—important outdoor recreational areas—appeared to have insufficient forest coverage within their 1000 m buffer zones. When forest accessibility was measured by travel time under optimal modes of transport; it was less than 15 min for most urban lands (~95%), which accommodates 98% of the total urban population. More importantly; the travel time to the nearest forest was negatively correlated with gross domestic product density (GDPd), but not with population density (POPd). The GDPd and POPd; however; increased log-linearly with the Euclidean distance to the nearest forest. In addition to the low proximity to forests; there existed inequalities among urban residents who live in areas with different levels of GDPd and POPd. Future urban planning needs not only to increase the total coverage of urban forests; but also to improve their spatial evenness across the urban landscapes in the PRD.

Landuse and land cover identification and disaggregating socio-economic data with convolutional neural network

We demonstrated an innovative learning method of convolutional neural network (CNN) to identify landuse and land cover (LULC) patterns and extract features to disaggregate socio-economic factors by using remote sensing imageries at 30 m spatial resolution. The training labels were extracted from the historical LULC map to reduce the huge cost of labelling work, and to provide an inaccuracy but sufficient training dataset. The fully connected layer of the trained CNN was extracted as disaggregating features to map socio-economic factors of population and gross domestic product (GDP). Results indicate that current method can attain 92% overall agreement of LULC identification with the cross-validation of other products. The determination coefficient of disaggregating socio-economic factors can reach 0.945 for population density, and 0.876 for GDP density with the cross-validation at county level.

Identification of spatial variation in road network and its driving patterns: Economy and population

China is a globally important ecosystem that is undergoing rapid development and land use/cover change (LUCC). The role of road networks in the LUCC is becoming increasingly important. Much of the upgrading in road networks is propelled by economy and population growth. However, the relationships between the road networks and the social-economic factors are poorly understood by using the ordinary least squares (OLS) regression, which assumes that the estimated beta value holds the same everywhere within a given study area. To determine whether there is spatial variation in the relationship between the road networks and the social-economic drivers in a given region, we employed a local model, geographically weighted regression (GWR), that provides a regression coefficient (beta) for each sample location within the study area. Taking Fujian Province, one of the most developed regions in China, as a case, this paper firstly employed an Exploratory Spatial Data Analysis (ESDA) to identify the spatial patterns of the road networks at the different sizes of sampling units. We found that the spatial distribution of road networks had an obvious tendency toward the geographical dependency, with High-High clusters seated in the eastern coastal areas and Low-Low clusters distributed dispersedly in the study area. The spatial patterns of the road networks had highly consistency in the different sizes of sample units. Then, the GWR model confirmed that the relationships between the road networks and the socioeconomic factors varied by locations, with multiple relationships (both synergy and tradeoff) between them coexisting in the study region. These relationships were also visualized by sign and magnitude of the estimated coefficients of GWR for each sampling unit, which allow us to compare the different effects of the dependent variables on the road networks across locations. We also found that the variable of gross domestic product density (GDPD) is a preferred indicator over the variable of population density (PD) to analyze the associations with the road networks. Thus, this study provided useful information to develop differentiated strategies for road development at the finer level by using the more nuanced GWR models.

Improving water quality in China: Environmental investment pays dividends

This study highlights how Chinese economic development detrimentally impacted water quality in recent decades and how this has been improved by enormous investment in environmental remediation funded by the Chinese government. To our knowledge, this study is the first to describe the variability of surface water quality in inland waters in China, the affecting drivers behind the changes, and how the government-financed conservation actions have impacted water quality. Water quality was found to be poorest in the North and the Northeast China Plain where there is greater coverage of developed land (cities + cropland), a higher gross domestic product (GDP), and higher population density. There are significant positive relationships between the concentration of the annual mean chemical oxygen demand (COD) and the percentage of developed land use (cities + cropland), GDP, and population density in the individual watersheds (p < 0.001). During the past decade, following Chinese government-financed investments in environmental restoration and reforestation, the water quality of Chinese inland waters has improved markedly, which is particularly evident from the significant and exponentially decreasing GDP-normalized COD and ammonium (NH4+-N) concentrations. It is evident that the increasing GDP in China over the past decade did not occur at the continued expense of its inland water ecosystems. This offers hope for the future, also for other industrializing countries, that with appropriate environmental investments a high GDP can be reached and maintained, while simultaneously preserving inland aquatic ecosystems, particularly through management of sewage discharge.

Euromind‐: A Density Estimate of Monthly Gross Domestic Product for the Euro Area

SummaryEuroMInd‐ is a density estimate of monthly gross domestic product (GDP) constructed according to a bottom‐up approach, pooling the density estimates of 11 GDP components, by output and expenditure type. The components' density estimates are obtained from a medium‐size dynamic factor model handling mixed frequencies of observation and ragged‐edged data structures. They reflect both parameter and filtering uncertainty and are obtained by implementing a bootstrap algorithm for simulating from the distribution of the maximum likelihood estimators of the model parameters, and conditional simulation filters for simulating from the predictive distribution of GDP. Both algorithms process the data sequentially as they become available in real time. The GDP density estimates for the output and expenditure approach are combined using alternative weighting schemes and evaluated with different tests based on the probability integral transform and by applying scoring rules. Copyright © 2016 John Wiley &amp; Sons, Ltd.

EuroMInd-D: A Density Estimate of Monthly Gross Domestic Product for the Euro Area

EuroMInd-D is a density estimate of monthly gross domestic product (GDP) constructed according to a bottom-up approach, pooling the density estimates of eleven GDP components, by output and expenditure type. The components density estimates are obtained from a medium-size dynamic factor model of a set of coincident time series handling mixed frequencies of observation and ragged-edged data structures. They reflect both parameter and filtering uncertainty and are obtained by implementing a bootstrap algorithm for simulating from the distribution of the maximum likelihood estimators of the model parameters, and conditional simulation filters for simulating from the predictive distribution of GDP. Both algorithms process sequentially the data as they become available in real time. The GDP density estimates for the output and expenditure approach are combined using alternative weighting schemes and evaluated with different tests based on the probability integral transform and by applying scoring rules.

Estimation of Gross Domestic Product Using Multi-Sensor Remote Sensing Data: A Case Study in Zhejiang Province, East China

There exists a spatial mismatch between socioeconomic data, such as Gross Domestic Product (GDP), and physical and environmental datasets. This study provides a dasymetric approach for GDP estimation at a fine scale by combining the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) nighttime imagery, enhanced vegetation index (EVI), and land cover data. Despite the advantages of DMSP/OLS nighttime imagery in estimating human activities, its drawbacks, including coarse resolution, overglow, and saturation effects, limit its application. Hence, high-resolution EVI data were integrated with DMSP/OLS in this study to create a Human Settlement Index (HSI) for estimating the GDP of secondary and tertiary industries. The GDP of the primary industry was then estimated on the basis of land cover data, and the area with the GDP of the primary industry was classified by a threshold technique (DN ≤ 8). The regression model for GDP distribution estimation was implemented in Zhejiang Province in southeast China, and a GDP density map was generated at a resolution of 250 m × 250 m. Compared with the outcome of taking DMSP/OLS as a unique parameter, estimation errors obviously decreased. This study offers a low-cost and accurate approach for rapidly estimating high-resolution GDP distribution to construct an important database for the government when formulating developmental strategies.

Spatial determinants of hazardous chemicals in surface water of Qiantang River, China

Spatial regression, incorporating spatial error or lag dependency, was performed to interpret determinants of hazardous chemicals at full sub-basin scale and at 500m riparian buffer scale in Qiantang River, eastern coastal China. Monitoring data from 41 monitoring stations were collected between 1996 and 2003 and pretreated for 7 variables—petroleum, hexavalent chromium, total cadmium, total lead, total mercury, total cyanide, and volatile phenol. Results showed that primary predictors and the predictive ability of spatial regression differed with variables and scales. Topology, distance to river source, land use/land cover (LULC), population density, and gross domestic product (GDP) can be primary predictors for the pattern of certain hazardous chemical variables in 1996 and 2003. LULC types were good predictors for changes of cyanide and heavy metals, while GDP and population density contributed to petroleum dynamics between 1996 and 2003. This study demonstrated that spatial regression is a promising tool for generating indicators to tackle with hazardous chemical pollution. We also advocate applying multi-scale approaches to uncover the dynamics of hazardous chemicals.