Big data technology has become increasingly prevalent in facilitating the delivery of government services and public goods in China and beyond.This article seeks to examine how big data can be leveraged in poverty gov...Big data technology has become increasingly prevalent in facilitating the delivery of government services and public goods in China and beyond.This article seeks to examine how big data can be leveraged in poverty governance in rural China,and what the political implications are.This phenomenon of big data-driven welfare governance is particularly salient considering the broader context of governmental digital transformation,both within China and globally.This research sheds light on how big data usage functions in poverty alleviation,highlighting general motivations and developments in Chinese data-centric welfare governance.More importantly,this article introduces the novel concept of"digital cybernetic capacity"to examine public sector modernization as big data transforms the landscape of welfare delivery and governance.By delving into the intersection between technology and social welfare,we explicate how the use of big data in social welfare policies can go beyond merely improving information capacity to redefine state capacity.This article argues that such a new governance ecosystem,driven as it is by data technologies,not only provides insights about the transformative resilience of Chinese governance but also opens a new theoretical frontier for research into other countries.展开更多
Law enforcement remains to be the main strategy used to combat poaching and account for high budget share in protected area management. Studies on efficiency of wildlife law enforcement in the protected areas are limi...Law enforcement remains to be the main strategy used to combat poaching and account for high budget share in protected area management. Studies on efficiency of wildlife law enforcement in the protected areas are limited. This study analyzed economic efficiency of wildlife law enforcement in terms of resource used and output generated using three different protected areas (PAs) of Serengeti ecosystem namely Serengeti National Park (SENAPA), Ikorongo/Grumeti Game Reserves (IGGR) and Ikona Wildlife Management Area (IWMA). Three years (2010-2012) monthly data on wildlife law enforcement inputs and outputs were collected from respective PAs authorities and supplemented with key informant interviews and secondary data. Questionnaire surveys were conducted to wildlife law enforcement staff. Shadow prices for non-marketed inputs were estimated, and market prices for marketed inputs. Data Envelopment Analysis (DEA) was used to estimate economic efficiency using Variable Return to Scale (VRS) and Constant Return to Scale (CCR) assumptions. Results revealed that wildlife law enforcement in all PAs was economically inefficient, with less inefficiency observed in IWMA. The less inefficiency in IWMA is likely attributed to existing sense of ownership and responsibility created through community-based conservation which resulted in to decrease in law enforcement costs. A slacks evaluation revealed a potential to reduce fuel consumption, number of patrol vehicles, ration and prosecution efforts at different magnitudes between studied protected areas. There is equal potential to recruit more rangers while maintaining the resting time. These finding forms the bases for monitoring and evaluation with respect to resource usage to enhance efficiency. It is further recommended to enhance community participation in conservation in SENAPA and IGGR to lower law enforcement costs. Collaboration between protected area, police and judiciary is fundamental to enhance enforcement efficiency. Despite old dataset, these findings are relevant since neither conservation policy nor institution framework has changed substantially in the last decade.展开更多
基金the youth project"Research on the Practical Challenges of Government Digital Transformation and Their Mitigation Mechanisms from the Perspective of Algorithmic Governance"supported by the National Social Science Fund of China(No.21CZZ039).
文摘Big data technology has become increasingly prevalent in facilitating the delivery of government services and public goods in China and beyond.This article seeks to examine how big data can be leveraged in poverty governance in rural China,and what the political implications are.This phenomenon of big data-driven welfare governance is particularly salient considering the broader context of governmental digital transformation,both within China and globally.This research sheds light on how big data usage functions in poverty alleviation,highlighting general motivations and developments in Chinese data-centric welfare governance.More importantly,this article introduces the novel concept of"digital cybernetic capacity"to examine public sector modernization as big data transforms the landscape of welfare delivery and governance.By delving into the intersection between technology and social welfare,we explicate how the use of big data in social welfare policies can go beyond merely improving information capacity to redefine state capacity.This article argues that such a new governance ecosystem,driven as it is by data technologies,not only provides insights about the transformative resilience of Chinese governance but also opens a new theoretical frontier for research into other countries.
文摘Law enforcement remains to be the main strategy used to combat poaching and account for high budget share in protected area management. Studies on efficiency of wildlife law enforcement in the protected areas are limited. This study analyzed economic efficiency of wildlife law enforcement in terms of resource used and output generated using three different protected areas (PAs) of Serengeti ecosystem namely Serengeti National Park (SENAPA), Ikorongo/Grumeti Game Reserves (IGGR) and Ikona Wildlife Management Area (IWMA). Three years (2010-2012) monthly data on wildlife law enforcement inputs and outputs were collected from respective PAs authorities and supplemented with key informant interviews and secondary data. Questionnaire surveys were conducted to wildlife law enforcement staff. Shadow prices for non-marketed inputs were estimated, and market prices for marketed inputs. Data Envelopment Analysis (DEA) was used to estimate economic efficiency using Variable Return to Scale (VRS) and Constant Return to Scale (CCR) assumptions. Results revealed that wildlife law enforcement in all PAs was economically inefficient, with less inefficiency observed in IWMA. The less inefficiency in IWMA is likely attributed to existing sense of ownership and responsibility created through community-based conservation which resulted in to decrease in law enforcement costs. A slacks evaluation revealed a potential to reduce fuel consumption, number of patrol vehicles, ration and prosecution efforts at different magnitudes between studied protected areas. There is equal potential to recruit more rangers while maintaining the resting time. These finding forms the bases for monitoring and evaluation with respect to resource usage to enhance efficiency. It is further recommended to enhance community participation in conservation in SENAPA and IGGR to lower law enforcement costs. Collaboration between protected area, police and judiciary is fundamental to enhance enforcement efficiency. Despite old dataset, these findings are relevant since neither conservation policy nor institution framework has changed substantially in the last decade.
