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Over 3 million Chinese Rural Vehicles (CRVs) were produced in China in 2002, three times that of conventional passenger cars. Yet these smaller, simpler, indigenous vehicles are virtually unknown outside China. The CRV industry is unusual in that it evolved largely outside the control of government regulation and policy, using local technology and resources. CRVs now consume one fourth of the diesel fuel in China and play an important role in rural development. This paper is the first comprehensive assessment (in English or Chinese) of these vehicles and this remarkable industry. This study documents and analyzes vehicle technology, government policy, environmental impacts, market demand, and industry dynamics. We find that increasing government regulation (mostly for emissions and safety) is having profound effects on the industry, with uncertain implications for the sales and globalization of rural vehicle technology.

This paper describes the economically optimal adoption and operation of distributed energy resources (DER) by a hypothetical California microgrid (μGrid) consisting of a group of commercial buildings over an historical test year, 1999. The optimization is conducted using a customer adoption model developed at Berkeley Lab and implemented in the General Algebraic Modeling System. A μGrid is a semiautonomous grouping of electricity and heat loads interconnected with the existing utility grid (macrogrid) but able to island from it. The μGrid minimizes the cost of meeting its energy requirements (consisting of both electricity and heat loads) by optimizing the installation and operation of DER technologies while purchasing residual energy from the local combined natural gas and electricity utility. The available DER technologies are small-scale generators (<500 kW), such as reciprocating engines, microturbines, and fuel cells, with or without combined heat and power (CHP) equipment, such as water and space heating and/or absorption cooling. By introducing a tax on carbon emissions, it is shown that if the μGrid is allowed to install CHP-enabled DER technologies, its carbon emissions are mitigated more than without CHP, demonstrating the potential benefits of small-scale CHP technology for climate change mitigation. Reciprocating engines with heat recovery and/or absorption cooling tend to be attractive technologies for the mild southern California climate, but the carbon mitigation tends to be modest compared to purchasing utility electricity because of the predominance of relatively clean central station generation in California.

Many proponents of organic farming claim that it is a sustainable alternative to conventional agriculture due to its reliance on natural agro-inputs, such as manure based fertilizers and organic pesticides. However, in this analysis we argue that although particular organic farming practices clearly benefit ecosystems and human consumers, the social context in which some organic farms develop, limit the potential environmental benefits of organic agriculture. Specifically, we argue that certified organic farming’s increased reliance on agro-inputs, such as organic fertilizers and pesticides, reduces its ability to decrease global water pollution. We review recent research that demonstrates the environmental consequences of specific organic practices, as well as literature showing that global organic farming is increasing its reliance on agro-inputs, and contend that organic farming has its own metabolic rift with natural water systems similar to conventional agriculture. We use a fixed-effects panel regression model to explore how recent rises in certified organic farmland correlate to water pollution (measured as biochemical oxygen demand). Our findings indicate that increases in the proportion of organic farmland over time increases water pollution. We conclude that this may be a result of organic farms increasing their reliance on non-farm agro-inputs, such as fertilizers.

In recent years, a growing body of research has investigated the factors influencing land surface temperature (LST) in different cities, employing diverse methodologies. Our study aims to be one of the few to examine the socio-environmental variables (SV) of LST with a holistic approach, especially in primate cities in developing countries, which are particularly vulnerable to the impacts of climate change. In this context, the study preliminarily identifies the SV of LST while investigating the most vulnerable areas related to extreme LST at the neighborhood level. The combined 11 variables are analyzed using spatial modeling methods (GWR and MGWR). The MGWR model outperforms the GWR model with an adjusted R2 of 0.96. The results showed that: (1) the 65+ population is negatively associated with LST in 95% of neighborhoods; the socioeconomic index–LST relationship is negative in 65% of neighborhoods. (2) In 90% of the neighborhoods where the relationship between LST and the built environment ratio is positive, the socioeconomic level decreases while household size increases in 98% of the neighborhoods. (3) In 62% of the neighborhoods where the relationship between the 65+ population and LST is negative, the relationship between the socioeconomic level and LST is negative. This study aids decision-makers and planners in managing urban resources to reduce extreme LST exposure region by region and recommending multiscale policies to control determinant influences on LST.

AbstractA robust biomedical informatics infrastructure is essential for academic health centers engaged in translational research. There are no templates for what such an infrastructure encompasses or how it is funded. An informatics workgroup within the Clinical and Translational Science Awards network conducted an analysis to identify the scope, governance, and funding of this infrastructure. After we identified the essential components of an informatics infrastructure, we surveyed informatics leaders at network institutions about the governance and sustainability of the different components. Results from 42 survey respondents showed significant variations in governance and sustainability; however, some trends also emerged. Core informatics components such as electronic data capture systems, electronic health records data repositories, and related tools had mixed models of funding including, fee-for-service, extramural grants, and institutional support. Several key components such as regulatory systems (e.g., electronic Institutional Review Board [IRB] systems, grants, and contracts), security systems, data warehouses, and clinical trials management systems were overwhelmingly supported as institutional infrastructure. The findings highlighted in this report are worth noting for academic health centers and funding agencies involved in planning current and future informatics infrastructure, which provides the foundation for a robust, data-driven clinical and translational research program.

This report analyzed the potential for increasing energy efficiency and reducing greenhouse gas emissions (GHGs) in the non-residential building and the industrial sectors in India. The first two sections describe the research and analysis supporting the establishment of baseline energy consumption using a bottom up approach for the non residential sector and for the industry sector respectively. The third section covers the explanation of a modeling framework where GHG emissions are projected according to a baseline scenario and alternative scenarios that account for the implementation of cleaner technology.

Quantifying Pollutant Emissions from Office Equipment Phase I Report Literature Review, Screening Level Measurements, and Revised Phase-II Research Proposal Principal Investigator: Thomas E. McKone Co Investigators: S.K. Hammond and A.T. Hodgson Authors R.L. Maddalena H. Destaillats A.T. Hodgson T.E. McKone C. Perino Prepared for: State of California Air Resources Board Research Division PO Box 2815 Sacramento CA 95812 Prepared by: Environmental Health Sciences Division School of Public Health 140 Warren Hall, #7360 University of California Berkeley, CA 94720-7360 December 2006

The rapid growth of “big data” provides tremendous opportunities for making better decisions, where “better” can be defined using any combination of economic, environmental, or social metrics. This essay provides a few examples of how the use of big data can precipitate more sustainable decision‐making. However, as with any technology, the use of big data on a large scale will have some undesirable consequences. Some of these are foreseeable, while others are entirely unpredictable. This essay highlights some of the sustainability‐related challenges posed by the use of big data. It does not intend to suggest that the advent of big data is an undesirable development. However, it is not too early to start asking what the unwanted repercussions of the big data revolution might be.