Using culturomics and social media data to characterize wildlife consumption

Wildlife provides food, medicine, clothing, and other necessities for humans, but overexploitation can disrupt the sustainability of wildlife resources and severely threaten global biodiversity. Understanding the characteristics of consumer behavior is helpful for wildlife managers and policy makers, but the traditional survey methods are laborious and time-consuming. In contrast, culturomics may more efficiently identify the features of wildlife consumption. As a case study of the culturomics approach, we examined tiger bone wine consumption in China based on social media and Baidu search engine data. Tiger bone wine is one of the most purchased tiger products; its consumption is closely related to tiger poaching, which greatly threatens wild tiger survival. We searched a popular social media website for the term “tiger bone wine” and focused on posts that were originally created from 1 January 2012 to 31 December 2018. We filtered and classified posts related to the purchase, sale, or consumption of tiger bone wine and extracted information on providers, consumption motivations, year of production, and place of origin of the tiger bone wines based on the texts and photos of these posts. We found 756 posts related to tiger bone wine consumption, 113 of which mentioned providers of tiger bone wine, including friends (53%), elder relatives (37%), peer relatives (7%), and others (3%). Out of the 756 posts, 266 indicated the motivations of tiger bone wine consumption. Tiger bone wines were consumed as a tonic (34%), medicine (23%), game product (30%), and a symbol of wealth (28%). Some posts indicated ≥2 consumption motivations. These findings were consistent with the search queries from Baidu index. Such information could help develop targeted strategies for tiger conservation. The culturomics approach illustrated by our study is a rapid and cost-efficient way to characterize wildlife consumption.     

企业社会责任:推动可持续发展的第三种力量

可持续发展是企业社会责任的重要领域。企业社会责任在推动可持续发展方面发挥着独特的作用。一定程度上弥补了政府干预和市场调节的缺陷。我国企业承担可持续发展责任需要企业和社会的互动。     

Prioritization of Sustainable Groundwater Management Needs: The Case of the Israel's Stressed Coastal Aquifer

On-going population growth and resulting domestic demand for water require rapid and effective decision-making as regards groundwater management and control of the various sources of salinization and pollution in Coastal aquifers. Sustainability of water resources for utilization by future generations must therefore be a high priority, not only for the purpose of fulfilling needs for water usage but also for bringing people into harmony with their ambient natural environment.The objective of this paper is to propose an empirical approach for prioritization of the needs involved for sustainable aquifer management. The approach involves a schematic format to:(1) develop a global understanding of an aquifer's hydrological and environmental properties in order to delineate appropriate eco-hydrological scenarios and recommend corresponding operational management activities; and(2) emphasize the importance of educating and increasing the awareness of the population involved as to the need for and viability of socially acceptable measures for sustainable management of groundwater and other resources.The psychologist Abraham Maslow utilized a pyramid to illustrate that until people's most basic needs were fulfilled, higher levels of needs would remain irrelevant. This paper postulates a comparable pyramid prioritizing hydrological needs required for progressing towards sustainable groundwater resources. Two sub-regions of Israel's Coastal aquifer in the Sharon region have been presented as representative areas, each characterized by different stress of exploitation. In assessing these sub-regions situation, specific measures have been recommended for achieving and/or maintaining sustainable groundwater resources in light of the ambient environment, and the level of the population on the pyramidal hierarchy of groundwater needs.     

社会和谐与可持续发展

本文运用系统科学原理阐释了社会和谐的内涵,指出社会和谐须以人与自然和谐为基础,以人与人之间的平等、友爱和同合作为表征,其构建与实现应遵从人类社会的演化规律,依赖予在把握人类社会需求演绎、协同人与自然关系的基础上推动生产力的高效有序发展和自然资源、生态环境的可持续支持.进而从可持续发展角度,提出了人与自然和谐的"双剩余理论",以揭示其间的供瓣枧理和协同准则;推导出了社会收益分配的相对公平系数,以衡量人与人公平要求下的社会和谐.针对国情和战略目标要求,提出了以加强小城镇建设为轴心的城市、小城镇与农村人流、物流、财流和信息流合理聚散的"三元结构"发展模式,以促进我国和谐社会的建设和可持续发展战略的有序实践.     

SUSTAINABILITY AT THE LOCAL LEVEL: MANAGEMENT TOOLS AND MUNICIPAL TAX INCENTIVE MODEL

The implementation of sustainable development may seem a simple concept when written on paper. However to carry-out long term actions put forward by the Agenda 21 (AG21) at the local level represents one of the main challenges as municipal governments in general do not have the capacity to effectively implement the process. Regional environmental assessment (REA) has shown to be effective in supporting decision-making not only to correct environmental problems due to past unsustainable social-economic developments but also help local governments to implement sustainable actions. However this requires long-term investments of AG21 plans and projects. The allocation of regular and consistent financial resources is one of the main ingredients for the sustainable development process. But traditional plans and projects financed by national and/or international funds may not be sustainable in the long-term because they become dependent on external funding. Research demonstrate that innovative economic instruments such as ecotaxes represent a feasible alternative to sponsor local sustainability because taxes are collected permanently by the government and could be invested in continuous actions. Ecotaxes experiences have provided important reference to structure a municipal incentive model (MIM) to sponsor AG21’s environmental plans and projects on a long-term (permanent) basis. However sustainable development cannot be solely through economic investments. A comprehensive municipal environmental management scheme (MEMS) has been established to support the incentive model. The scheme seeks not only to improve local institutional framework but also incentive continuous participation of local stakeholders at all levels of society. Participatory events and the provision of incentives (educational and financial) are key to motivate society to protect the environment and support actively the sustainable development process as emphasised in the RIO-92 Conference.     

Particulate matter exchange between atmosphere and roads surfaces in urban areas

The deposition and the re-suspension of particulate matter (PM) in urban areas are the key processes that contribute not only to stormwater pollution, but also to air pollution. However, investigation of the deposition and the re-suspension of PM is challenging because of the difficulties in distinguishing between the resuspended and the deposited PM. This study created two Bayesian Networks (BN) models to explore the deposition and the re-suspension of PM as well as the important influential factors. The outcomes of BN modelling revealed that deposition and re-suspension of PM10 occurred under both, high-traffic and low-traffic conditions, and the re-suspension of PM2.5 occurred under low-traffic conditions. The deposition of PM10 under low-volume traffic condition is 1.6 times higher than under high-volume traffic condition, which is attributed to the decrease in PM10 caused by relatively higher turbulence under high-volume traffic conditions. PM10 is more easily resuspended from road surfaces compared to PM2.5 as the particles which larger than the thickness of the laminar airflow over the road surface are more easily removed from road surfaces. The increase in wind speed contributes to the increase in PM build-up by transporting particulates from roadside areas to the road surfaces and the airborne PM2.5 and PM10 increases with the increase in relative humidity. The study outcomes provide a step improvement in the understanding of the transfer processes of PM2.5 and PM10 between atmosphere and urban road surfaces, which in turn will contribute to the effective design of mitigation measures for urban stormwater and air pollution.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Economic development and environmental protection: an ecological economics perspective

This paper argues on both theoretical and empirical grounds that, beyond a certain point, there is an unavoidable conflictbetween economic development (generally taken to mean 'materialeconomic growth') and environmental protection. Think for a moment of natural forests, grasslands, marine estuaries, salt marshes, and coral reefs; and of arable soils, aquifers, mineraldeposits, petroleum, and coal. These are all forms of 'natural capital' that represent highly-ordered self-producing ecosystemsor rich accumulations of energy/matter with high use potential (low entropy). Now contemplate despoiled landscapes, eroding farmlands, depleted fisheries, anthropogenic greenhouse gases,acid rain, poisonous mine tailings and toxic synthetic compounds.These all represent disordered systems or degraded forms of energy and matter with little use potential (high entropy). The main thing connecting these two states is human economic activity. Ecological economics interprets the environment-economyrelationship in terms of the second law of thermodynamics. The second law sees economic activity as a dissipative process. Fromthis perspective, the production of economic goods andservices invariably requires the consumption of available energy and matter. To grow and develop, the economynecessarily 'feeds' on sources of high-quality energy/matter first produced by nature. This tends to disorder and homogenizethe ecosphere, The ascendance of humankind has consistently been accompanied by an accelerating rate of ecological degradation, particularly biodiversity loss, the simplificationof natural systems and pollution. In short, contemporary political rhetoric to the contrary, the prevailing growth-oriented global development paradigm is fundamentally incompatible with long-term ecological and social sustainability. Unsustainability is not a technical nor economic problem as usually conceived, but rather a state of systemic incompatibilitybetween a economy that is a fully-contained, growing, dependent sub-system of a non-growing ecosphere. Potential solutions fly inthe face of contemporary development trends and cultural values.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Selecting Socio-Economic Metrics for Watershed Management

The selection of social and economic metrics to document baseline conditions and analyze the dynamic relationships between ecosystems and human communities are important decisions for scientists, managers, and watershed citizens. A large variety of social and economic data is available but these have limited use without theoretical frameworks. In this paper, several frameworks for reviewing social-ecosystem relations are offered, namely social sanctions, sense of place, civic structure, and cultural differences. Underlying all of these frameworks are attitudes, beliefs, values, and norms that affect which questions are asked and which indicators are chosen. Much work and significant challenges remain in developing a standard set of spatially based socio-economic metrics for watershed management.