scholarly journals Depreciation of Capital due to Natural Disaster and Adjusted Net Domestic Product

2020 ◽  
Author(s):  
Amarendra Das ◽  
Dasarathi Padhan ◽  
Chinmayee Sahoo
Keyword(s):  
Time Series ◽  
Economic Policy ◽  
Natural Disasters ◽  
Policy Making ◽  
Economic Loss ◽  
Methodological Framework ◽  
Indian States ◽  
Systematic Data ◽  
Approximate Estimate ◽  
Fiscal Transfer

<p>This paper tries to account for the economic loss due to natural disasters, as the depreciation of capital and provide the adjusted estimate of NDP. Systematic data on economic loss due to natural disasters are limited for all Indian states. Therefore, this paper provides a methodological framework for accounting for the depreciation of the capital due to natural disasters for all Indian states and provides an approximate estimate of adjusted NDP for all Indian states. Due to availability of systematic data on the economic loss due to natural disasters, time series estimate of adjusted NDP are provided for Odisha state which experiences maximum climatic natural disasters. Calculation of adjusted NDP will be very much useful for fiscal transfer from Union to States and other economic policy making. </p>

scholarly journals Depreciation of Capital due to Natural Disaster and Adjusted Net Domestic Product

2020 ◽  
Author(s):  
Amarendra Das ◽  
Dasarathi Padhan ◽  
Chinmayee Sahoo
Keyword(s):  
Time Series ◽  
Economic Policy ◽  
Natural Disasters ◽  
Policy Making ◽  
Economic Loss ◽  
Methodological Framework ◽  
Indian States ◽  
Systematic Data ◽  
Approximate Estimate ◽  
Fiscal Transfer

<p>This paper tries to account for the economic loss due to natural disasters, as the depreciation of capital and provide the adjusted estimate of NDP. Systematic data on economic loss due to natural disasters are limited for all Indian states. Therefore, this paper provides a methodological framework for accounting for the depreciation of the capital due to natural disasters for all Indian states and provides an approximate estimate of adjusted NDP for all Indian states. Due to availability of systematic data on the economic loss due to natural disasters, time series estimate of adjusted NDP are provided for Odisha state which experiences maximum climatic natural disasters. Calculation of adjusted NDP will be very much useful for fiscal transfer from Union to States and other economic policy making. </p>

scholarly journals Can Mandated Political Representation Increase Policy Influence for Disadvantaged Minorities? Theory and Evidence from India

2003 ◽  
Vol 93 (4) ◽  
pp. 1132-1151 ◽  
Author(s):  
Rohini Pande
Keyword(s):  
Policy Making ◽  
Political Representation ◽  
Representative Democracy ◽  
Indian States ◽  
Basic Premise ◽  
Policy Influence ◽  
Disadvantaged Groups ◽  
Institutional Features

A basic premise of representative democracy is that all those subject to policy should have a voice in its making. However, policies enacted by electorally accountable governments often fail to reflect the interests of disadvantaged minorities. This paper exploits the institutional features of political reservation, as practiced in Indian states, to examine the role of mandated political representation in providing disadvantaged groups influence over policy-making. I find that political reservation has increased transfers to groups which benefit from the mandate. This finding also suggests that complete policy commitment may be absent in democracies, as is found in this case.

scholarly journals Learning from Nighttime Observations of Gas Flaring in North Dakota for Better Decision and Policy Making

2021 ◽  
Vol 13 (5) ◽  
pp. 941
Author(s):  
Rong Lu ◽  
Jennifer L. Miskimins ◽  
Mikhail Zhizhin
Keyword(s):  
Time Series ◽  
North Dakota ◽  
Policy Making ◽  
Nearest Neighbor ◽  
Negative Binomial ◽  
Infrared Imaging ◽  
Gaussian Mixture Models ◽  
Data Gathering ◽  
Gaussian Mixture ◽  
Estimation Errors

In today’s oil industry, companies frequently flare the produced natural gas from oil wells. The flaring activities are extensive in some regions including North Dakota. Besides company-reported data, which are compiled by the North Dakota Industrial Commission, flaring statistics such as count and volume can be estimated via Visible Infrared Imaging Radiometer Suite nighttime observations. Following data gathering and preprocessing, Bayesian machine learning implemented with Markov chain Monte Carlo methods is performed to tackle two tasks: flaring time series analysis and distribution approximation. They help further understanding of the flaring profiles and reporting qualities, which are important for decision/policy making. First, although fraught with measurement and estimation errors, the time series provide insights into flaring approaches and characteristics. Gaussian processes are successful in inferring the latent flaring trends. Second, distribution approximation is achieved by unsupervised learning. The negative binomial and Gaussian mixture models are utilized to describe the distributions of field flare count and volume, respectively. Finally, a nearest-neighbor-based approach for company level flared volume allocation is developed. Potential discrepancies are spotted between the company reported and the remotely sensed flaring profiles.

Spatial-Temporal Dynamics of Disease Burden Attributable to Pm2.5 Exposure in China From 2000 to 2016

2021 ◽  
Author(s):  
Ting Cao
Keyword(s):  
Policy Making ◽  
Disease Burden ◽  
Temporal Dynamics ◽  
Comprehensive Evaluation ◽  
Fine Particulate Matter ◽  
Economic Loss ◽  
Northwest China ◽  
Economic Losses ◽  
Air Pollution Control ◽  
Chinese Yuan

Abstract In recent years, long-term exposure to ambient fine particulate matter (PM2.5) has slowly increased both morbidity and mortality for Chinese people, becoming a leading problem for public health efforts. However, spatial-temporal dynamics of disease burden attributable to PM2.5 exposure still lacks a comprehensive evaluation so as to provide inadequate supports for policy making and improvement. Here, we used the exposure-response function to derive the spatial-temporal dynamics of disease burden attributable to PM2.5 pollution in China. We found the fact that economic loss attributable to PM2.5 increased by 93% from 35 billion Chinese Yuan (95% CI: 14-52) to 536 billion Chinese Yuan (95%CI: 236-753) during the period of 16 years. Digging further, we discovered a substantiate level of regional differences, with the disease burden being the most severe in East China and the least severe in the Northwest China. Other than that, there existed a spatial aggregation of health-related economic losses among Chinese cities. Our paper made an evaluation on the spatial-temporal dynamics of health effects attributed to PM2.5, an evaluation that could provide more insights to future policy making of the air pollution control for China and other developing countries.

scholarly journals Multiparty government and economic policy-making

Public Choice ◽  
2016 ◽  
Vol 170 (1-2) ◽  
pp. 33-62 ◽  
Author(s):  
Hanna Bäck ◽  
Wolfgang C. Müller ◽  
Benjamin Nyblade
Keyword(s):  
Economic Policy ◽  
Policy Making
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