Science
27 November 2015 vol 350, issue 6264, pages 1001-1124
http://www.sciencemag.org/current.dtl

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Policy Forum
Energy and Environment
Understanding China’s non–fossil energy targets
Joanna I. Lewis, David G. Fridley, Lynn K. Price, Hongyou Lu, and John P. Romankiewicz
Science 27 November 2015: 1034-1036.
Methodology standardization will improve comparability
Summary
More than 130 countries have targets for increasing their share of renewable or nonfossil energy (1). These shares and targets are often reported without clear articulation of which energy accounting method was used to convert nonfossil electricity into units that allow comparison with other energy sources (2–4). Three commonly used conversion methods are well documented by organizations dealing in energy statistics, but often, the method is not clearly stated when countries translate national targets into international pledges or when organizations track and compare targets across nations. China—the world’s largest energy producer, energy consumer, and emitter of energy-related carbon dioxide (CO2)—uses a distinct fourth method that is unique, not well documented in the literature, and not transparent in policy documents. A single, standardized, and transparent methodology for any targets that are pledged as part of an international agreement is essential.

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Report
Predicting poverty and wealth from mobile phone metadata
Joshua Blumenstock1,*, Gabriel Cadamuro2, Robert On3
Author Affiliations
1Information School, University of Washington, Seattle, WA 98195, USA.
2Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA.
3School of Information, University of California, Berkeley, Berkeley, CA 94720, USA.
Abstract
Accurate and timely estimates of population characteristics are a critical input to social and economic research and policy. In industrialized economies, novel sources of data are enabling new approaches to demographic profiling, but in developing countries, fewer sources of big data exist. We show that an individual’s past history of mobile phone use can be used to infer his or her socioeconomic status. Furthermore, we demonstrate that the predicted attributes of millions of individuals can, in turn, accurately reconstruct the distribution of wealth of an entire nation or to infer the asset distribution of microregions composed of just a few households. In resource-constrained environments where censuses and household surveys are rare, this approach creates an option for gathering localized and timely information at a fraction of the cost of traditional methods.