{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/81627"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/81627","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"China Urban Pollution Information Disclosure Study (CUPIDS) : socioeconomic implications of dirty industry and a guide to national cleandustrialization","abstract":"It is now common knowledge that China's data is bad, and China's environment is polluted. In this paper I develop a simple pollution intensity index to test China's existing national and local environmental data, to answer the questions: \"Bad\" how? \"Polluted\" why? And finally: what can be done? I find that China's existing data is rudimentary, piecemeal, and inaccurate. I find that government data is not detailed, not transparent, and that non-governmental attempts at improving data quality and transparency are hamstringed by lack of official support. These data quality and transparency issues contribute to the intractability of China's industrial pollution. I also find that a pollution intensity index, like the one I develop and test in this study, can help policymakers identify pollution hotspots years before the hotspots worsen into public health emergencies. By running 2010 data through a simple algorithm, I isolate a pollution hotspot that took the Chinese government until 2013 to discover without this framework. I show that better and more accurate data, along with a mandate for continuous monitoring and analysis, can shift the current strategy from pollution control to pollution prevention, ultimately saving time, money, and lives.","abstract_html":"It is now common knowledge that China's data is bad, and China's environment is polluted. In this paper I develop a simple pollution intensity index to test China's existing national and local environmental data, to answer the questions: "Bad" how? "Polluted" why? And finally: what can be done? I find that China's existing data is rudimentary, piecemeal, and inaccurate. I find that government data is not detailed, not transparent, and that non-governmental attempts at improving data quality and transparency are hamstringed by lack of official support. These data quality and transparency issues contribute to the intractability of China's industrial pollution. I also find that a pollution intensity index, like the one I develop and test in this study, can help policymakers identify pollution hotspots years before the hotspots worsen into public health emergencies. By running 2010 data through a simple algorithm, I isolate a pollution hotspot that took the Chinese government until 2013 to discover without this framework. I show that better and more accurate data, along with a mandate for continuous monitoring and analysis, can shift the current strategy from pollution control to pollution prevention, ultimately saving time, money, and lives.","abstract_has_math":false,"creators":["Chu, Yang, M.C.P. Massachusetts Institute of Technology"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Urban Studies and Planning.","school":null,"contributors":[],"advisors":["Karen R. Polenske."],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013","date_published":"2013","updated_at":"2026-07-22T22:21:46Z","subjects":["Urban Studies and Planning."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. 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In this paper I develop a simple pollution intensity index to test China's existing national and local environmental data, to answer the questions: \"Bad\" how? \"Polluted\" why? And finally: what can be done? I find that China's existing data is rudimentary, piecemeal, and inaccurate. I find that government data is not detailed, not transparent, and that non-governmental attempts at improving data quality and transparency are hamstringed by lack of official support. These data quality and transparency issues contribute to the intractability of China's industrial pollution. I also find that a pollution intensity index, like the one I develop and test in this study, can help policymakers identify pollution hotspots years before the hotspots worsen into public health emergencies. By running 2010 data through a simple algorithm, I isolate a pollution hotspot that took the Chinese government until 2013 to discover without this framework. I show that better and more accurate data, along with a mandate for continuous monitoring and analysis, can shift the current strategy from pollution control to pollution prevention, ultimately saving time, money, and lives."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.C.P."]},{"key":"dc:title","label":"Title","values":["China Urban Pollution Information Disclosure Study (CUPIDS) : socioeconomic implications of dirty industry and a guide to national cleandustrialization"]}]}],"canonical_facts":{"dc:contributor.advisor":["Karen R. Polenske."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Urban Studies and Planning."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Urban Studies and Planning."],"dc:creator":["Chu, Yang, M.C.P. 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These data quality and transparency issues contribute to the intractability of China's industrial pollution. I also find that a pollution intensity index, like the one I develop and test in this study, can help policymakers identify pollution hotspots years before the hotspots worsen into public health emergencies. By running 2010 data through a simple algorithm, I isolate a pollution hotspot that took the Chinese government until 2013 to discover without this framework. I show that better and more accurate data, along with a mandate for continuous monitoring and analysis, can shift the current strategy from pollution control to pollution prevention, ultimately saving time, money, and lives."],"dc:description.degree":["M.C.P."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/81627"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. 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