ESTIMATING INDUSTRIAL STRUCTURE CHANGES IN CHINA USING DMSP – OLS NIGHT-TIME LIGHT DATA DURING 1999–2012

The Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) night-time light imagery has been proved to be a powerful tool to monitor economic development with its relatively high spatial resolution at large scales. Night-time lights caused by human activities derived from DMSP-OLS satellite imagery are widely used in socioeconomic parameter estimations and urbanization monitoring. In this paper, DMSP-OLS night-time stable light data from 1999 to 2012 are utilized to analyze inter-annual variation in GDP of per unit light intensity (RGDP) in China. Furthermore, RGDP was compared with statistical data of the tertiary industry structure for 28 provincial regions. The results show that the provincial RGDP decreased abruptly in 2001–2002, 2008–2009 and 2011–2012, which is consistent with the proportional growth of the tertiary industry in GDP. These results indicate that the changes in RGDP can reflect tertiary industry structural changes in China's province-level regions

Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light Data

China is one of the largest carbon emitting countries in the world. Numerous strategies have been considered by the Chinese government to mitigate carbon emissions in recent years. Accurate and timely estimation of spatiotemporal variations of city-level carbon emissions is of vital importance for planning of low-carbon strategies. For an assessment of the spatiotemporal variations of city-level carbon emissions in China during the periods 2000–2017, we used nighttime light data as a proxy from two sources: Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data and the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The results show that cities with low carbon emissions are located in the western and central parts of China. In contrast, cities with high carbon emissions are mainly located in the Beijing-Tianjin-Hebei region (BTH) and Yangtze River Delta (YRD). Half of the cities of China have been making eorts to reduce carbon emissions since 2012, and regional disparities among cities are steadily decreasing. Two clusters of high-emission cities located in the BTH and YRD followed two dierent paths of carbon emissions owing to the diverse political status and pillar industries. We conclude that carbon emissions in China have undergone a transformation to decline, but a very slow balancing between the spatial pattern of high-emission versus low-emission regions in China can be presumed

Modeling macroeconomic indicators in unstable economies

A method for analysis of the dynamics of macroeconomic indicators based on the model of a piecewise trend for economies of unstable growth is proposed. The relevance of the article is supported by the absence of adequate mathematical models and the inadequacy of traditional continuous models to describe the features of economic dynamics of this type. Its application is demonstrated on the examples of Ukraine, Greece and Italy in comparison with stable developing countries of Eastern Europe - the Czech Republic, Slovakia and Poland. In the process of approbation new indices of instability based on this model have been developed. A higher degree of conformity of the proposed model is proved in comparison with traditional continuous models not only for countries with signs of unstable economic dynamics, but also for some countries with stable economies. During approbation, a new index of instability of growth was developed based on this piecewise linear trend model. The indices of instability of growth were calculated for 43 European countries for the period from 1989 to 2019 and their rating was built

Modeling the spatiotemporal dynamics of gross domestic product in China using extended temporal coverage nighttime light data

Nighttime light data derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) in conjunction with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) possess great potential for measuring the dynamics of Gross Domestic Product (GDP) at large scales. The temporal coverage of the DMSP-OLS data spans between 1992 and 2013, while the NPP-VIIRS data are available from 2012. Integrating the two datasets to produce a time series of continuous and consistently monitored data since the 1990s is of great significance for the understanding of the dynamics of long-term economic development. In addition, since economic developmental patterns vary with physical environment and geographical location, the quantitative relationship between nighttime lights and GDP should be designed for individual regions. Through a case study in China, this study made an attempt to integrate the DMSP-OLS and NPP-VIIRS datasets, as well as to identify an optimal model for long-term spatiotemporal GDP dynamics in different regions of China. Based on constructed regression relationships between total nighttime lights (TNL) data from the DMSP-OLS and NPP-VIIRS data in provincial units (R2 = 0.9648, P < 0.001), the temporal coverage of nighttime light data was extended from 1992 to the present day. Furthermore, three models (the linear model, quadratic polynomial model and power function model) were applied to model the spatiotemporal dynamics of GDP in China from 1992 to 2015 at both the country level and provincial level using the extended temporal coverage data. Our results show that the linear model is optimal at the country level with a mean absolute relative error (MARE) of 11.96%. The power function model is optimal in 22 of the 31 provinces and the quadratic polynomial model is optimal in 7 provinces, whereas the linear model is optimal only in two provinces. Thus, our approach demonstrates the potential to accurately and timely model long-term spatiotemporal GDP dynamics using an integration of DMSP-OLS and NPP-VIIRS data

Nutrition transition in Thailand: An empirical study using night-time lights : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University

Nutrition transition is defined as changes in dietary patterns. Recently, researchers have observed nutrition transition in developing countries experiencing higher incomes and urbanisation. This study aims to better understand this phenomenon in Thailand by using econometric analyses. We use the Quadratic Almost Ideal Demand System (QUAIDS) model to investigate changes in food expenditure over time, and the direct approach, a reduced-form model, is used for nutrient intake. The study uses food expenditure and nutrient intake data from the Thailand Socio-Economic Survey (SES) compiled by Thailand’s National Statistical Office (NSO). This study also investigates how urbanisation changes patterns in food expenditure and nutrient intake using satellite images of night-time lights obtained from the National Oceanic and Atmospheric Administration (NOAA). The study first examines why Thai households change their dietary pattern. The results show that nutrition transition is specifically linked to dynamic changes in household demographics and geographic characteristics. Household demographics include household size, household composition and women’s labour force participation. These household demographics with geographic characteristics clearly explain the shift in dietary patterns. Interestingly, women participating in the labour force increases the frequency of home-cooked meals, leading to better nutrition and wellbeing for Thai families. Thus, demographics and geographic characteristics are as equally significant as economic factors in the analysis. Second, this study discusses how changes in dietary patterns have occurred in Thailand. Changes in food and nutrient intake patterns are complex. Consistent with the framework for nutrition transition, food preferences have shifted towards animal-source foods, sugary products and ready-cooked food. However, traditional staples, such as grain and cereal products, remain a dominant source of calories. Healthy foods (such as fruit and nuts) are no longer a luxury, reflecting higher familiarity with these foods among Thai households; nutrition inequality materializes in Thailand with less access to these healthy foods among the poor. Sin goods (such as sugary products) are more desirable, particularly in households with children and in poor households. Together, the results confirm that nutrition transition is happening in Thailand. Finally, this study investigates the effects of urbanisation on nutrition transition. The results indicate that night-time lights highlight the role of urbanisation in shaping patterns of dietary intake; increased urbanisation stimulates food diversity. This makes the demand for food and nutrients less income-elastic, which can create a permanent change in some food preferences. Urbanisation, therefore, drives new eating habits in Thailand