Emergy-based comparative analysis on industrial clusters: economic and technological development zone of Shenyang area,China

In China, local governments of many areas prefer to give priority to the development of heavy industrial clusters in pursuit of high value of gross domestic production (GDP) growth to get political achievements, which usually results in higher costs from ecological degradation and environmental pollution. Therefore, effective methods and reasonable evaluation system are urgently needed to evaluate the overall efficiency of industrial clusters. Emergy methods links economic and ecological systems together, which can evaluate the contribution of ecological products and services as well as the load placed on environmental systems. This method has been successfully applied in many case studies of ecosystem but seldom in industrial clusters. This study applied the methodology of emergy analysis to perform the efficiency of industrial clusters through a series of emergy-based indices as well as the proposed indicators. A case study of Shenyang Economic Technological Development Area (SETDA) was investigated to show the emergy method’s practical potential to evaluate industrial clusters to inform environmental policy making. The results of our study showed that the industrial cluster of electric equipment and electronic manufacturing produced the most economic value and had the highest efficiency of energy utilization among the four industrial clusters. However, the sustainability index of the industrial cluster of food and beverage processing was better than the other industrial clusters.     

Do renewable energies contribute to enhancing environmental quality in Eastern Africa?

East Africa has enormous renewable energy potential, but only a small portion of it has been exploited, and little is known on its role in improving environmental quality. Thus, this study empirically examines the impact of renewable energy on the environment using ecological footprint (EF; positive indicator) and CO₂ emissions (negative indicator) as proxy indicators for environmental quality in a panel of ten East African countries from 1990 to 2015. These indicators were chosen due to their potential impact in the environment. The work used the pooled mean group (PMG) as the main panel estimator to determine the impact while controlling non-renewable energy consumption, GDP per capita, and foreign direct investment (FDI). PMG has been used as it forces the long-run coefficients to be equal across all panel groups. The findings show that in the long run, there is a significant negative relationship between CO₂ emissions and renewable energy consumption, as well as a significant positive relationship (with a low impact) between EF and renewable energy consumption, suggesting that renewable energy use enhances the area’s environmental quality. Also, results indicate that non-renewable energy use degrades environmental quality in both metrics, whereas GDP degrades environmental quality through CO₂ emissions and improves environmental quality through EF. This requires East African countries to focus a higher emphasis on accessible renewable energy sources to achieve quick and sustainable economic growth and minimize environmental effects. To accomplish this, strategic policies and legislation, as well as the promotion of green technology, are required.

     

Emergy-based assessment on industrial symbiosis: a case of Shenyang Economic and Technological Development Zone

Industrial symbiosis is the sharing of services, utility, and by-product resources among industries. This is usually made in order to add value, reduce costs, and improve the environment, and therefore has been taken as an effective approach for developing an eco-industrial park, improving resource efficiency, and reducing pollutant emission. Most conventional evaluation approaches ignored the contribution of natural ecosystem to the development of industrial symbiosis and cannot reveal the interrelations between economic development and environmental protection, leading to a need of an innovative evaluation method. Under such a circumstance, we present an emergy analysis-based evaluation method by employing a case study at Shenyang Economic and Technological Development Zone (SETDZ). Specific emergy indicators on industrial symbiosis, including emergy savings and emdollar value of total emergy savings, were developed so that the holistic picture of industrial symbiosis can be presented. Research results show that nonrenewable inputs, imported resource inputs, and associated services could be saved by 89.3, 32.51, and 15.7 %, and the ratio of emergy savings to emergy of the total energy used would be about 25.58 %, and the ratio of the emdollar value of total emergy savings to the total gross regional product (GRP) of SETDZ would be 34.38 % through the implementation of industrial symbiosis. In general, research results indicate that industrial symbiosis could effectively reduce material and energy consumption and improve the overall eco-efficiency. Such a method can provide policy insights to industrial park managers so that they can raise appropriate strategies on developing eco-industrial parks. Useful strategies include identifying more potential industrial symbiosis opportunities, optimizing energy structure, increasing industrial efficiency, recovering local ecosystems, and improving public and industrial awareness of eco-industrial park policies.     

Roadmap for climate alliance economies to vision 2030: retrospect and lessons

The United Nations Climate Conference 25, held in December 2019, reached a significant agreement against implementing the Paris agreement come 2020. Bound by the contract, 189 countries who are party to the deal agreed to constrain worldwide temperature to ascend to 1.5° Celsius. To this end, the present study attempts to investigate the readiness of selected countries in the European Union to implement the agreement, which will better the quality of the global environment. In line with this, this study appraises the connection between economic growth, renewable and non-renewable energy consumption, on emissions in 11 countries in the European Union from 1990 to 2016. The study utilises the Pooled Mean Group-Auto Regressive Distributed Lag (PMG-ARDL) model estimator and Dumitrescu and Hurlin Panel Causality analysis to analyse the long-run and short-run impact and direction of causality among these factors, respectively. The long-run study's empirical results show a U-shaped Environmental Kuznets Curve (EKC) and a negative connection between renewable energy use and emissions in the EU-11 countries. In the short-run, non-renewable energy use worsens CO₂ emissions while renewable energy use leads to a fall in emissions. Similarly, causality tests show a feedback mechanism between emissions and renewable energy use and between non-renewable energy and renewable use. Also, there is unidirectional causality from income to CO₂ emissions, non-renewable energy use to CO₂ emissions. The investigation recommends an expanded proportion of renewable energy sources in the EU countries’ energy mix to cut down on emissions.

     

Carbon dioxide emissions,output, and energy consumption categories in Algeria

This study examines the relation between CO₂ emissions, income, non-renewable, and renewable energy consumption in Algeria during the period extending from 1980 to 2011. Our work gives particular attention to the validity of environmental Kuznets curve (EKC) hypothesis. The autoregressive distributed lag (ARDL) with break point method outcome demonstrates the positive effect of non-renewable type of energy on CO₂ emissions consumption. On the contrary, the results reveal an insignificant effect of renewable energy on environment improvement. Moreover, the results accept the existence of EKC hypothesis but the highest gross domestic product value in logarithm scale of our data is inferior to the estimated turning point. Consequently, policy-makers in Algeria should expand the ratio of renewable energy and should decrease the quota of non-renewable energy consumption.     

Modeling habitat suitability for moose in coastal northern Sweden: empirical vs process-oriented approaches

Habitat models may provide viable tools for co-management of large ungulates and forest resources, yet their applicability has not been comprehensively evaluated in managed forest. We examined 2 inherently different approaches to model the relative winter habitat suitability for moose (Alces alces) in the coastal area of northern Sweden. An empirical approach based on GPS positions of 15 female moose was used to scrutinize the assumptions and functional mechanisms of a process-oriented, conceptual approach, based on published material on the species' preferences for habitat components related to food and cover. For both model approaches habitat was described using estimates of forest-stand characteristics based on satellite imagery. The empirical model also included variables relating to topographic properties of the landscape as well as distances to landscape features. The output from both models was a habitat suitability index (HSI) score, enabling the models to be compared with each other. The models showed different results, highlighting the need to include the spatially explicit distribution of environmental variables in future conceptual, process-oriented models.     

Environmental sustainability through renewable energy and banking sector development: policy implications for N-11 countries

Environmental Science and Pollution Research - Climate change has become an alarming condition for developed and developing countries. The main reason for this is the use of non-renewable energy...     

Application of satellite remote-sensing data for source analysis of fine particulate matter transport events

Satellite sensors have provided new datasets for monitoring regional and urban air quality. Satellite sensors provide comprehensive geospatial information on air quality with both qualitative imagery and quantitative data, such as aerosol optical depth. Yet there has been limited application of these new datasets in the study of air pollutant sources relevant to public policy. One promising approach to more directly link satellite sensor data to air quality policy is to integrate satellite sensor data with air quality parameters and models. This paper presents a visualization technique to integrate satellite sensor data, ground-based data, and back trajectory analysis relevant to a new rule concerning the transport of particulate matter across state boundaries. Overlaying satellite aerosol optical depth data and back trajectories in the days leading up to a known fine particulate matter with an aerodynamic diameter of <2.5 microm (PM2.5) event may indicate whether transport or local sources appear to be most responsible for high PM2.5 levels in a certain location at a certain time. Events in five cities in the United States are presented as case studies. This type of analysis can be used to help understand the source locations of pollutants during specific events and to support regulatory compliance decisions in cases of long distance transport.     

Burning Water: A Comparative Analysis of the Energy Return on Water Invested

While various energy-producing technologies have been analyzed to assess the amount of energy returned per unit of energy invested, this type of comprehensive and comparative approach has rarely been applied to other potentially limiting inputs such as water, land, and time. We assess the connection between water and energy production and conduct a comparative analysis for estimating the energy return on water invested (EROWI) for several renewable and non-renewable energy technologies using various Life Cycle Analyses. Our results suggest that the most water-efficient, fossil-based technologies have an EROWI one to two orders of magnitude greater than the most water-efficient biomass technologies, implying that the development of biomass energy technologies in scale sufficient to be a significant source of energy may produce or exacerbate water shortages around the globe and be limited by the availability of fresh water.     

Soil metal concentrations and productivity of Betula populifolia (gray birch) as measured by field spectrometry and incremental annual growth in an abandoned urban Brownfield in New Jersey

A forested brownfield within Liberty State Park, Jersey City, New Jersey, USA, has soils with arsenic, chromium, lead, zinc and vanadium at concentrations above those considered ambient for the area. Using both satellite imagery and field spectral measurements, this study examines plant productivity at the assemblage and individual specimen level. Longer term growth trends (basal area increase in tree cores) were also studied. Leaf chlorophyll content within the hardwood assemblage showed a threshold model for metal tolerance, decreasing significantly beyond a soil total metal load (TML) of 3.0. Biomass production (calculated with RG - Red/Green Ratio Index) in Betula populifolia (gray birch), the co-dominant tree species, had an inverse relationship with the Zn concentration in leaf tissue during the growing season. Growth of B. populifolia exhibited a significant relationship with TML. Assemblage level NDVI and individual tree NDVI also had significant decreases with increasing TML. Ecosystem function measured as plant production is impaired at a critical soil metal load.     

Application of Satellite Remote-Sensing Data for Source Analysis of Fine Particulate Matter Transport Events

Abstract

Satellite sensors have provided new datasets for monitoring regional and urban air quality. Satellite sensors provide comprehensive geospatial information on air quality with both qualitative imagery and quantitative data, such as aerosol optical depth. Yet there has been limited application of these new datasets in the study of air pollutant sources relevant to public policy. One promising approach to more directly link satellite sensor data to air quality policy is to integrate satellite sensor data with air quality parameters and models. This paper presents a visualization technique to integrate satellite sensor data, ground-based data, and back trajectory analysis relevant to a new rule concerning the transport of particulate matter across state boundaries. Overlaying satellite aerosol optical depth data and back trajectories in the days leading up to a known fine particulate matter with an aerodynamic diameter of <2.5 μm (PM_2.5) event may indicate whether transport or local sources appear to be most responsible for high PM_2.5 levels in a certain location at a certain time. Events in five cities in the United States are presented as case studies. This type of analysis can be used to help understand the source locations of pollutants during specific events and to support regulatory compliance decisions in cases of long distance transport.     

Trilemma association of energy consumption,carbon emission,and economic growth of BRICS and OECD regions: quantile regression estimation

Environmental Science and Pollution Research - Recent research has shown a huge impact of non-renewable energy (NRE) production on environmental health. In this context, this work analyzes the...     

Using satellite imagery to locate innovative forest management practices in Nepal

There is a critical need to locate innovative forest management institutions that significantly impact forest cover change. This research presents an initial "proof of concept" methodology which combines deforestation theory with satellite image change analysis to identify forested areas that, theoretically, should probably not be there. Ten such "forest anomalies" are identified using temporal analysis of Landsat TM imagery of the Chitwan district in Nepal, linked with a GIS database on roads and a visual estimation of topography. A rapid field reconnaissance is undertaken to determine which of these anomalies exhibit interesting forest management innovations. Based on this information, one case is selected for detailed field study: this turns out to be a major case of community forestry and a premier ecotourism initiative that we were not aware of until we undertook this analysis. The utility and limitations of the method are described for monitoring trends in forest cover change.     

Managing Fleet Capacity Effectively Under Second-Hand Market Redistribution

Fishing capacity management policies have been traditionally implemented at national level with national targets for capacity reduction. More recently, capacity management policies have increasingly targeted specific fisheries. French fisheries spatially vary along the French coastline and are associated to specific regions. Capacity management policies, however, ignore the capital mobility associated with second-hand vessel trade between regions. This is not an issue for national policies but could limit the effectiveness of regional capacity management policies. A gravity model and a random-effect Poisson regression model are used to analyze the determinants and spatial extent of the second-hand market in France. This study is based on panel data from the French Atlantic Ocean between 1992 and 2009. The trade flows between trading partners is found to increase with their sizes and to be spatially concentrated. Despite the low trade flows between regions, a net impact analysis shows that fishing capacity is redistributed by the second-hand market to regions on the Channel and Aquitaine from central regions. National capacity management policies (constructions/destructions) have induced a net decrease in regional fleet capacity with varying magnitude across regions. Unless there is a change of policy instruments or their scale of implementation, the operation of the second-hand market decreases the effectiveness of regional capacity management policies in regions on the Channel and Aquitaine.     

How does biased technological progress affect haze pollution? Evidence from APEC economies

Biased technological progress is the act of energy conservation and emission reduction by changing the marginal rate of substitution. In this study, we introduced renewable energy into a production function, and proposed a method of identifying biased characteristics of technological progress, based on marginal productivity theory. A panel dataset for the Asia–Pacific Economic Cooperation (APEC) economies from 2000 to 2017 was analyzed to explore the effect of biased technological progress in reducing particulate matter (PM_2.5). We found that input biased technological progress tended to use more non-renewable energy. Input biased technological progress aggravated haze pollution; however, this effect decreased as the PM_2.5 concentration increased. Output biased technological progress significantly reduced haze pollution in high-income economies, but increased it in low-income economies. The effect of neutral technological progress on haze pollution was the opposite of the effect from output biased technological progress. We also found that increasing renewable energy consumption and reducing energy intensity were separate effective paths for input and output biased technological progress, respectively, to mitigate haze pollution. For neutral technological progress, improving total factor productivity was an important way to mitigate haze pollution. Finally, several policy recommendations are proposed to mitigate haze pollution in APEC economies.

     

Recommendations on the use of satellite remote-sensing data for urban air quality

In the last 5 yr, the capabilities of earth-observing satellites and the technological tools to share and use satellite data have advanced sufficiently to consider using satellite imagery in conjunction with ground-based data for urban-scale air quality monitoring. Satellite data can add synoptic and geospatial information to ground-based air quality data and modeling. An assessment of the integrated use of ground-based and satellite data for air quality monitoring, including several short case studies, was conducted. Findings identified current U.S. satellites with potential for air quality applications, with others available internationally and several more to be launched within the next 5 yr; several of these sensors are described in this paper as illustrations. However, use of these data for air quality applications has been hindered by historical lack of collaboration between air quality and satellite scientists, difficulty accessing and understanding new data, limited resources and agency priorities to develop new techniques, ill-defined needs, and poor understanding of the potential and limitations of the data. Specialization in organizations and funding sources has limited the resources for cross-disciplinary projects. To successfully use these new data sets requires increased collaboration between organizations, streamlined access to data, and resources for project implementation.     

Detection of Asian dust (Hwangsa) over the Yellow Sea by decomposition of unpolarized infrared reflectivity

The periodical occurrence of Asian dust (Hwangsa) in East Asia during every spring and winter is of great consequence to regional human health and visibility. One weakness of the previous methods based on the brightness temperature (BT) differences or optical depths is that they can not accurately detect the dust events over the Yellow Sea. This study proposes a unique technique of decomposing unpolarized reflectivities to distinguish between dust and clouds over the sea surface. Many current infrared (IR) satellite instruments such as MTSAT-1R measure the unpolarized radiance. In this process, an approximate relationship between vertically and horizontally polarized reflectivities is validated for the IR wavelengths of 3–12 μm. The radiances observed from an IR geostationary satellite and the simulated sea surface temperature (SST) are used for a case study of Asian dust events. The results show qualitatively the reasonable spatial agreements with MODIS true color images for these Asian dust events. The proposed method in this study can be utilized for the operational purpose to detect the signal of Asian dust over sea surface.     

Recommendations on the Use of Satellite Remote-Sensing Data for Urban Air Quality

Abstract

In the last 5 yr, the capabilities of earth-observing satellites and the technological tools to share and use satellite data have advanced sufficiently to consider using satellite imagery in conjunction with ground-based data for urban-scale air quality monitoring. Satellite data can add synoptic and geospatial information to ground-based air quality data and modeling. An assessment of the integrated use of ground-based and satellite data for air quality monitoring, including several short case studies, was conducted. Findings identified current U.S. satellites with potential for air quality applications, with others available internationally and several more to be launched within the next 5 yr; several of these sensors are described in this paper as illustrations. However, use of these data for air quality applications has been hindered by historical lack of collaboration between air quality and satellite scientists, difficulty accessing and understanding new data, limited resources and agency priorities to develop new techniques, ill-defined needs, and poor understanding of the potential and limitations of the data. Specialization in organizations and funding sources has limited the resources for cross-disciplinary projects. To successfully use these new data sets requires increased collaboration between organizations, streamlined access to data, and resources for project implementation.     

Integrating lidar and satellite optical depth with ambient monitoring for 3-dimensional particulate characterization

A combination of in-situ PM_2.5, sunphotometers, upward pointing lidar and satellite aerosol optical depth (AOD) instruments have been employed to better understand variability in the correlation between AOD and PM_2.5 at the surface. Previous studies have shown good correlation between these measures, especially in the US east, and encouraged the use of satellite data for spatially interpolating between ground sensors. This work shows that cases of weak correlation can be better understood with knowledge of whether the aerosol is confined to the surface planetary boundary layer (PBL) or aloft. Lidar apportionment of the fraction of aerosol optical depth that is within the PBL can be scaled to give better agreement with surface PM_2.5 than does the total column amount. The study has shown that lidar combined with surface and remotely sensed data might be strategically used to improve our understanding of long-range or regionally transported pollutants in multiple dimensions.     

Efficient retrieval of vegetation leaf area index and canopy clumping factor from satellite data to support pollutant deposition assessments

Canopy leaf area index (LAI) is an important structural parameter of the vegetation controlling pollutant uptake by terrestrial ecosystems. This paper presents a computationally efficient algorithm for retrieval of vegetation LAI and canopy clumping factor from satellite data using observed Simple Ratios (SR) of near-infrared to red reflectance. The method employs numerical inversion of a physics-based analytical canopy radiative transfer model that simulates the bi-directional reflectance distribution function (BRDF). The algorithm is independent of ecosystem type. The method is applied to 1-km resolution AVHRR satellite images to retrieve a geo-referenced data set of monthly LAI values for the conterminous USA. Satellite-based LAI estimates are compared against independent ground LAI measurements over a range of ecosystem types. Verification results suggest that the new algorithm represents a viable approach to LAI retrieval at continental scale, and can facilitate spatially explicit studies of regional pollutant deposition and trace gas exchange.