Search results

The non-observed economy (NOE) is a pervasive phenomenon worldwide, especially in developing countries, but the size of the NOE and its contributions to the overall economy are usually unknown. This paper presents an estimation of the average size of the NOE for the 31 provincial regions in China between 1992 and 2013.

This study uses the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data combined with 11 existing surveys on or measurements of NOE for 191 countries or regions throughout the world, to measure the size of the NOE.

The results show that the NOE share is unevenly distributed among China's provincial regions, with the smallest being 3.19% for Beijing and the largest being 69.71% for Ningxia. The national average is 43.11%, while the figures for the eastern region, middle region, northeastern region and western region are 39.3%, 47.6%, 44.7% and 43.6%, respectively. The NOE estimates are negatively correlated with the measured gross domestic product (GDP) and GDP per capita, which suggests that developed regions tend to have less NOE.

The nighttime lights are used to measure the NOE for China's provincial regions. Compared with traditional databases, one of the prominent features of nighttime lights is its objectivity, as there is little human interference; therefore, it can be used to achieve more accurate results.

The purpose of the study is to evaluate whether nighttime luminosity sourced from the Defense Meteorological Satellite Program-Operational Linescan System satellite sensors is a suitable proxy for measuring poverty in Africa.

Our study performs wavelet coherence analysis to investigate the time-frequency synchronization between the nightlight data and “income-to-wealth” ratio for 39 African countries between 1992 and 2012.

All-in-all, the authors find that approximately a third of African countries produce positive synchronizations between nighttime data and “income-to-wealth” ratio and hence conclude that most African countries are not at liberty to use nighttime data to proxy conventional poverty statistics.

In differing from previous studies, the authors examine the suitability of nightlight intensity as a proxy of poverty for individual African countries using much more rigorous analysis.

This chapter investigates how night-time light images acquired from the Defense Meteorological Satellite Program’s Operational Linescan System provide spatial and temporal insight into the economic impact of the disintegration of Yugoslavia. First, the chapter provides an overview of the economic development in Yugoslavia using conventional statistics, and second, it presents an analysis of the disintegration of the federation by comparing official statistics with night-time light data. Evaluating the impact of the disintegration of Yugoslavia as a federation and the conflicts arising in the wake of the break up is challenging since reliable data is missing. Therefore, satellite images, as one of the few sources of objective information, are potentially of great importance. We used yearly Operational Linescan System composites covering the period 1992–2013. The analysis is divided into small geographical units (districts) based on the republics in the former Yugoslavia.

In the last 50 years, the Mediterranean Basin has experienced a doubling of its population. This demographic growth has been the cause of extensive land use changes that have undermined the ecological stability of large portions of its fragile ecosystems. The population of the Mediterranean countries is expected to grow by another 20 percent in the next 25 years, further increasing the pressure on the natural resources. In this paper, we present a methodology combining photosynthetic activity and human settlements both derived from satellite data for monitoring the effects of human settlements on the environment. We found photosynthesis decreasing as one moves from rural to urban settings in the north and increasing in the south Mediterranean countries. Regional scale assessments using this approach may help policy makers in designing appropriate measures to combat further environmental degradation.

Carbon storage in protected land is a practical climate stabilization strategy. It is increasingly being recognized as an essential means of safeguarding biomass carbon and improving local ecological conditions. Yet, increasing soil carbon sequestration by setting aside nature reserves does not depend only on the scale of the reserve but more so on the implementation and enforcement of the reserve protection policy. This paper aims to discuss the aforementioned issues.

The authors show how nature reserves established and managed by different administrative levels affect carbon sequestration. Empirically, the authors estimate a time-varying difference-in-difference model that exploits China's distinct four-layered hierarchical nature reserve management system at the county level.

The findings show that higher administrative level (i.e. national and provincial) nature reserves have no effects on the carbon dynamic. However, reserves managed by lower administrative levels (i.e. prefecture- and county-level) are associated with reduced carbon sequestration. The results imply local governments fail to fulfil their responsibilities for nature reserves protection, leading to increased extractive activities and declined ecological biomass.

Responsibility and accountability mechanisms for the violation of the nature reserves requirements need to be stipulated accordingly. Greater emphasis should be placed on nature reserves at the base level. The central government should continue efforts toward the establishment of ad hoc and independent management agencies at the ground-management level that are free of influence from base-level governments.

The heterogeneity in the performance of nature reserves across administrative levels confirms that ecosystem service quality is highly dependent on establishment, management and supervision. This provides a better understanding of the socio-ecological interdependence of protected areas.

The spatial and non-spatial attributes are the two important characteristics of a spatial point, which belong to the two different attribute domains in many Geographic Information Systems applications. The dual clustering algorithms take into account both spatial and non-spatial attributes, where a cluster has not only high proximity in spatial domain but also high similarity in non-spatial domain. In a geographical dataset, traditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. To overcome this limitation, a novel dual-domain clustering algorithm (DDCA) is proposed by considering both spatial proximity and attribute similarity with the presence of inhomogeneity.

In this algorithm, Delaunay triangulation with edge length constraints is first employed to construct spatial proximity relationships amongst objects. Then, a clustering strategy based on statistical change detection is designed to obtain clusters with similar attributes.

The effectiveness and practicability of the proposed algorithm are illustrated by experiments on both simulated datasets and real spatial events. It is found that the proposed algorithm can adaptively and accurately detect clusters with spatial proximity and similar non-spatial attributes under the consideration of inhomogeneity.

Traditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. The research here is a contribution to developing a dual spatial clustering method considering both spatial proximity and attribute similarity with the presence of inhomogeneity. The detection of these clusters is useful to understand the local patterns of geographical phenomena, such as land use classification, spatial patterns research and big geo-data analysis.

China is by far the world’s largest energy consumer and importer. Reasonably forecasting the trend of China’s total energy consumption (CTEC) is of great significance. The purpose of this paper is to propose a new-structure grey system model (NSGM (1, 1)) to forecast CTEC.

Two matrices for computing the parameters of NSGM (1, 1) were defined and the specific calculation formula was derived. Since the NSGM (1, 1) model increases the number of its background values, which improves the smoothness effect of the background value and weakens the effects of extreme values in the raw sequence on the model’s performance; hence it has better simulation and prediction performances than traditional grey models. Finally, NSGM (1, 1) was used to forecast China’s total energy consumption during 2016-2025. The forecast showed CTEC will grow rapidly in the next ten years.

Therefore, in order to meet the target of keeping CTEC under control at 4.8 billion tons of standard coal in 2020, Chinese government needs to take necessary measures such as transforming the economic development pattern and enhancing the energy utilization efficiency.

A new-structure grey forecasting model, NSGM (1, 1), is proposed in this paper, which improves the smoothness and weakens the effects of extreme values and has a better structure and performance than those of other grey models. The authors successfully employ the new model to simulate and forecast CTEC. The research findings could aid Chinese government in formulating energy policies and help energy exporters make rational energy yield plans.

Aside from oil, the Nigerian economy is largely agrarian, which is rain-fed. Hence the criticality of understanding climate change and its impact on agricultural output is more pressing than ever. This is in line with Sustainable Development Goal 13 which is to take urgent action to combat climate change and its impacts. Regardless, Nigeria has in the past five decades experienced a significant increase in temperature, in the range of 10 to over 30 degree Celsius. Therefore, managing the effect of climate change on agricultural output now has the colouration of a developmental challenge.

In light of this, this study gives due consideration to the impact of climate change on agricultural output between the years 1986 and 2015. For the purpose of analysis, descriptive statistics, unit root test and the ordinary least square (OLS) estimation technique were employed.

Findings from the study reveal that the average annual rainfall, temperature and forest area positively influence agricultural output, whereas drought, floods and agricultural nitrous oxide (N₂O) emissions have negative impact on agricultural output. The study suggests the need for a regulatory framework and also an explicit national agricultural policy essential to offset the negative effects of climate change especially on agricultural output.

As Nigeria look to diversify her economy which relied on oil, agriculture is among the alternative sector hoping to drive her economic growth, therefore, it is pertinent to examine the current output in the sector given the effects of climate change.

This paper aims to analyze the spatiotemporal heterogeneity of regional total factor productivity (TFP) growth and explores how haze pollution and different levels of new-type urbanization affect China’s economic growth.

This paper constructs an index for evaluating the TFP growth of China’s 31 provinces by integrating slack-based measures and the Global Malmquist (GM) productivity index. Meanwhile, the panel threshold estimation method is used to examine the complex relationships among haze pollution, new-type urbanization and TFP growth.

The results reflect conspicuous spatiotemporal heterogeneity in TFP growth in China. Interestingly, the influence of haze pollution on TFP growth is limited by the “critical mass” of new-type urbanization in China. When new-type urbanization does not cross the first threshold, haze pollution has a negative but non-significant effect on TFP growth. When new-type urbanization crosses the first threshold but not the second, haze pollution has a significant positive impact on TFP growth. When new-type urbanization crosses the second threshold, haze pollution significantly and positively affects TFP growth with the strongest positive effect.

This study innovates by combining haze pollution and TFP growth and proposing an integrated framework from the perspective of new-type urbanization, providing insight into how different degrees of new-type urbanization impact the mechanism between haze pollution and TFP growth. Using panel data in China and emphasizing green development, a sustainable economy and new-type urbanization, this study contributes to the current studies on haze pollution and economic development based on developed countries.

System dynamics is a whole-system modelling and learning approach, useful for tackling non-linear problems, such as sustainable urban development. The purpose of this paper is to review system dynamics applications in the simulation of sustainable urban development over a period from 2005 to 2017.

The analysis reveals that the number of applications of system dynamics modelling in the area of urban sustainable development increased in the analysed period. Research has changed its focus from the modelling of environmental problems to more complex models, portraying the multidimensional socio-economic processes that have an impact on the sustainability of urban development. Analysed case studies most often use the behaviour reproduction test for model validation, but without a unified approach. In most cases, modelling has been done in China, Germany and the USA, while urban development in the Eastern European countries, Africa and Latin America has not often been investigated. This paper indicates the knowledge gaps and suggests future research directions.

Papers that report the use of system dynamics modelling reveal a wide range of applications in urban sustainability. The analysis shows significant emphasis on environmental problems, while the interest for modelling social problems has been increasing during the last several years. Most of the modelled problems examine the sustainability of resources (land, water) and waste management, which are used for insights into the reasons for the system behaviour, forecasting future behaviour and policy testing.

The presented models were developed in most cases for the purpose of understanding the phenomena examined, as well as the future use of the models in policy planning. This brings us back to the need for greater stakeholder involvement, not only in the initial phase, but also during the whole modelling process, which could increase understanding, use and ownership of the models in the future, and thus increase their practical application.