Emergy provides a general accounting mechanism that allows us to view the economy and the environment on the same income statement and balance sheet. This allows an auditor to verify the economic picture by checking it against a more complete representation of the flows and storages of real wealth as measured by emergy. In this study, we constructed emergy accounts for the state of West Virginia in 1997. The income statement showed annual production, consumption, and flows of emergy and dollars into and out of the state. The balance sheet evaluated the storage of emergy in some of the state's assets. Emergy indices were used to answer questions posed by managers and gain insight into the state's economic and environmental strengths and weaknesses. West Virginia has great wealth in nonrenewable resources (9E14 sej m-2 or 17 times the U.S. average). The investment ratio of emergy purchased outside to indigenous renewable and nonrenewable emergy was 2.2:1, which indicates a high potential for future development. However, the environmental loading ratio (14:1) was already 1.5 times higher than that found at an average location in the U.S. Twice as much emergy was exported as received and standard of living indicators showed that people have largely failed to benefit from their state's wealth. We propose that, just as in business, where decisions made using financial accounts ensure solvency; decisions governing the environment should be made based on an emergy accounting of activities, assets, and liabilities for the combined system of humanity and nature. 相似文献
The spread of highly pathogenic avian influenza H5N1 has posed a major threat to global public health. Understanding the spatiotemporal outbreak characteristics and environmental factors of H5N1 outbreaks is of great significance for the establishment of effective prevention and control systems. The time and location of H5N1 outbreaks in poultry and wild birds officially confirmed by the World Organization for Animal Health from 2005 to 2019 were collected. Spatial autocorrelation analysis and multidistance spatial agglomeration analysis methods were used to analyze the global outbreak sites of H5N1. Combined with remote sensing data, the correlation between H5N1 outbreaks and environmental factors was analyzed using binary logistic regression methods. We analyzed the correlation between the H5N1 outbreak and environmental factors and finally made a risk prediction for the global H5N1 outbreaks. The results show that the peak of the H5N1 outbreaks occurs in winter and spring. H5N1 outbreaks exhibit aggregation, and a weak aggregation phenomenon is noted on the scale close to 5000 km. Water distance, road distance, railway distance, wind speed, leaf area index (LAI), and specific humidity were protective factors for the outbreak of H5N1, and the odds ratio (OR) were 0.985, 0.989, 0.995, 0.717, 0.832, and 0.935, respectively. Temperature was a risk factor with an OR of 1.073. The significance of these ORs was greater than 95%. The global risk prediction map was obtained. Given that the novel coronavirus (COVID-19) is spreading globally, the methods and results of this study can provide a reference for studying the spread of COVID-19.
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