scholarly journals The Housing Market Impacts of Shale Gas Development

2014 ◽  
Author(s):  
Lucija Muehlenbachs ◽  
Elisheba Spiller ◽  
Christopher Timmins
Keyword(s):  
Housing Market ◽  
Shale Gas ◽  
Market Impacts

scholarly journals The Housing Market Impacts of Shale Gas Development

2015 ◽  
Vol 105 (12) ◽  
pp. 3633-3659 ◽  
Author(s):  
Lucija Muehlenbachs ◽  
Elisheba Spiller ◽  
Christopher Timmins
Keyword(s):  
Housing Market ◽  
Shale Gas ◽  
Water Source ◽  
Confounding Factors ◽  
Property Value ◽  
Well Productivity ◽  
Piped Water ◽  
Market Impacts ◽  
Negative Impacts ◽  
Using Data

Using data from Pennsylvania and an array of empirical techniques to control for confounding factors, we recover hedonic estimates of property value impacts from nearby shale gas development that vary with water source, well productivity, and visibility. Results indicate large negative impacts on nearby groundwater-dependent homes, while piped-water-dependent homes exhibit smaller positive impacts, suggesting benefits from lease payments. Results have implications for the debate over regulation of shale gas development. (JEL L71, Q35, Q53, R31)

The Housing Market Impacts of Shale Gas Development: Corrigendum

2016 ◽  
Vol 106 (2) ◽  
pp. 475-475 ◽  
Author(s):  
Lucija Muehlenbachs ◽  
Elisheba Spiller ◽  
Christopher Timmins
Keyword(s):  
Housing Market ◽  
Shale Gas ◽  
Market Impacts

scholarly journals The Housing Market Impacts of Shale Gas Development

2013 ◽  
Author(s):  
Lucija Muehlenbachs ◽  
Elisheba Spiller ◽  
Christopher Timmins
Keyword(s):  
Housing Market ◽  
Shale Gas ◽  
Market Impacts

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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