applying benefit-cost analysis to air pollution control in the indian power … · 2019-10-29 ·...

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Submit Manuscript | http://medcraveonline.com Abbreviations: AA, amino acid; BAP, bioactive peptide; BBB, blood brain barrier; BCAA, branched chain amino acid; BMI, body mass index; CCK, cholecystokinin; CHO, carbohydrate; DPP-IV, dipeptidyl peptidase IV; EWH, egg white hydrolysate; GI, gastro-intestinal tract; GLP-1, glucagon like peptide; GMP, glycomacropeptide; ICV, intracerebroventricular; IPP, isoleucine- proline-proline; kg, kilogram; m, meter; PYY, peptide YY; VPP, valine-proline-proline Introduction Obesity and its comorbidities are on the rise globally. Excessive calorie intake along with sedentary life style is major contributing factors in etiology of obesity. Therefore, understanding factors affecting appetite, satiety and also food selection is critically important. The mechanisms associated with food intake regulation originate from a sequence of complex interactions between the gastrointestinal (GI) tract and areas of the brain, particularly the hypothalamus and the brainstem so called “gut-brain axes”. 1 It includes the main neuro- endocrine regulatory pathways that control food intake in short- and long-term. 2 This integrated approach is designed to maintain metabolic homeostasis and also stabilize body weight and body composition in long-term by regulating energy and macro- and micro-nutrients’ intake in short- and long-term. Mechanisms regulating satiety can be categorized as short- and long-term. More than 40 hormones influence short-term food intake by regulating meal initiation, termination, size, and frequency primarily through GI-tract signals including cholecystokinin (CCK), Glucagon like peptide (GLP-1), peptide YY (PYY) and amylin as satiety signals and also ghrelin as a hunger signal2. Moreover, food intake regulatory signals including leptin and insulin influence long-term energy intake in response to current energy stores within the body in the form of fat deposits. Although these signals target different areas of brain, the hypothalamus is the predominant site of regulation. 2 Proteins suppress food intake more than fat and carbohydrate (calorie/calorie). 3 However, the underlying mechanisms are varied, complex and unclear. Individual properties of proteins including amino acid composition and sequence, digestion kinetics, digestibility and encrypted bioactive peptides within their amino acid sequences are potential factors influencing their physiologic and metabolic properties 4 and also determining their effect on food intake, satiety, and body weight in a source-dependent manner. Food sources of proteins There is a variety of proteins from a wide spectrum of food sources such as muscle, milk, egg, plant, pulse, soy, and other oil- producing plants. Fish, poultry, and red meat are major sources of muscle proteins. Based on their solubility they can be divided to 3 groups: 1- Sarcoplasmic such as myoglobin, 2- Myofibrillar such as myosin and actin and finally 3- Stromal proteins such as collagen and elastin. 5 Milk proteins contribute to up to 3.5% of cow milk. Casein and whey are the two major proteins in milk. They comprise about 80% and 20% of cow’s milk protein respectively while this proportion is reversed in human milk. Egg proteinscontribute to 13% of the whole egg content. Egg proteins include egg white proteins such as albumen and yolk. 5 Egg white is a good source of high quality protein; however, ovoinhibitor, a serine proteinase inhibitor in egg J Nutr Health Food Eng. 2017;6(6):186193. 186 © 2017 Greco et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and build upon your work non-commercially. The role of source of protein in regulation of food intake, satiety, body weight and body composition Volume 6 Issue 6 - 2017 Emily Greco, 1 Andrew Winquist, 2 Thomas Jacob Lee, 2 Shari Collins, 2 Zach Lebovic, 2 Tara Zerbe-Kessinger, 2 Alireza Jahan Mihan 2 1 Vincent’s Medical Center Southside, USA 2 Department of Nutrition and Dietetics, University of North Florida, USA Correspondence: Alireza Jahan Mihan, Department of Nutritional and Dietetics, Brooks College of Health, University of North Florida, 1 UNF Dr. BLDG 39, Room 3057A, Jacksonville, Florida, USA, Postal code 32224, Tel (904) 6205359, Fax (904) 6201942, Email [email protected] Received: May 30, 2017 | Published: July 27, 2017 Abstract Dietary proteins elicit a wide range of metabolic and physiologic functions that are far beyond their traditional role as sources of energy and also indispensable amino acids. Proteins play a significant role in the regulation of appetite, food intake, body weight, and body composition. Their role in regulation of blood pressure, glucose and lipid metabolism, bone metabolism, and immune system is also shown. The role of proteins as the most satiating macronutrient in regulation of food intake, body weight and body composition is well-studied. The direct interaction between dietary proteins and the gastro-intestinal (GI) tract via communication with endogenous satiety signals, in addition to their effects on protein synthesis are major underlying mechanisms. The interaction of proteins with the GI tract can be elucidated by their effect on gastric emptying rate, GI transport and absorption, GI-brain communication via gut-brain axis, and more recently by their effect on gut micro biota. However, there is increasing evidence indicating that these effects are source- dependent. Physicochemical properties of individual proteins, including digestion kinetics, digestibility, amino acid composition and sequence, bioactive peptides and also non-protein bioactive components conjugated with proteins are potential factors determining their physiologic and metabolic properties. Therefore, the purpose of this review is to examine the most recent findings about the role of the source of protein in regulation of food intake, body weight, and body composition. Keywords: dietary proteins, food intake, appetite, satiety, body weight, body composition Journal of Nutritional Health & Food Engineering Mini Review Open Access

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Page 1: Applying Benefit-Cost Analysis to Air Pollution Control in the Indian Power … · 2019-10-29 · Applying Benefit-Cost Analysis to Air Pollution Control in the Indian Power Sector

Applying Benefit-Cost Analysis to Air Pollution Control in the Indian

Power Sector

Maureen Cropper, Sarath Guttikunda, Puja Jawahar, Zach Lazri, Kabir Malik, Xiao-Peng Song, Xinlu Yao

BCA WorkshopOctober 22, 2019

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Page 2: Applying Benefit-Cost Analysis to Air Pollution Control in the Indian Power … · 2019-10-29 · Applying Benefit-Cost Analysis to Air Pollution Control in the Indian Power Sector

Motivation

• Coal-fired power plants in India generate 76% of electricity– Generating capacity doubled between 2008 and 2014– Grid-connected capacity currently 196 GW– Plans for an additional 114 GW by 2030

• Significant health impacts associated with coal plants – Global Burden of Disease (2018) estimated over 82,000 deaths

attributable to coal-fired power plants in 2015

• Can reduce health impacts by:– Installing flue-gas desulfurization units (scrubbers) – December 2015 regulations effectively require FGDs but few

plants have installed them

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Questions Addressed

• Does retrofitting coal-fired power plants in India with FGDs (scrubbers) pass the benefit-cost test?– In the aggregate?– At individual plants?

• How should the retrofitting of FDGs be prioritized?– One method is to prioritize plants based on net benefits of

retrofitting

• How sensitive are net benefits to the VSL and the discount rate?

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Our Research

• Cropper et al. DCP3, Injury Prevention and Environmental Health (2017)– Estimate cost per life saved of installing FGDs at 72 power

plants in operation in 2008-09 – Plant-by-plant study

• Cropper et al. Journal of Benefit-Cost Analysis (2019)– Estimate net benefits of scrubbing at 8 model power plants– Emphasizes impact of location on damages, with

implications for prioritizing retrofitting– Also impact of valuation methods on net benefits

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Page 5: Applying Benefit-Cost Analysis to Air Pollution Control in the Indian Power … · 2019-10-29 · Applying Benefit-Cost Analysis to Air Pollution Control in the Indian Power Sector

Coal-fired Power Plant Capacity 2016-17

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Coal-fired Power Plants 2008-2009

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Is Retrofitting FGDs Cost-Effective?

• We assume, conservatively, that scrubbers reduce deaths by 72% annually

• Using benchmark operating conditions, scrubbing at all plants would:– Save 13,000 lives per year (329,000 life years)– Average CPLS = $131,000 (2013$)– Average cost per life-year saved = $5,000– But CPLS varies from $25,000 to $1,200,000

• More efficient approaches suggested on next slide

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Cost-Effectiveness of FGD Installation, 2013$

Annual Annual AverageLives Cost Cost perSaved (Mil.) Life Saved

72 Plants 12,890 $1,691 $131,000

30 plants with lowest CPLS 9,196 $ 615 $ 67,000

30 plants with most deaths 10,061 $ 965 $ 96,000

30 largest7,910 $1,164 $147,000plants (MW)

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Location of Model Plants

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Benefit-Cost Analysis Framework

• Benefit Estimation:

– Estimate plant’s SO2 emissions– Translate emissions into impact on ambient PM2.5

– Estimate health impacts of ambient PM2.5

• Do this with and without the FGD

• Benefits = Monetized value of reduction in health impacts (PDV of health benefits over life of FGD)

• Subtract PDV of costs of operating the FGD over its lifetime from benefits to calculate net benefits

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Costs of Scrubbing

• Assume that a scrubber is retrofitted at each 500 MW model plant

– Seawater FGD for plants on the coast– Wet limestone FGD for all other plants

• Capital costs based on values for FGDs installed or in planning stages in India

• Model plant will be retrofitted with an FGD (raises capital costs by 30%) which has life of 20 years

• PDV of cost of wet limestone FGD are:– $165 million (2015$) at 3%; $127 million (2015$) at 8%

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Benefits of Scrubbing

• Calculate emissions of PM2.5, NOx, SO2 for each plant

• For each plant: estimate population-weighted ambient concentrations of PM2.5 associated with SO2 emissions– Use CAMx applied plant by plant; 2011 population data

• Use concentration-response functions from the Global Burden of Disease to estimate impact of SO2 emissions on stroke, IHD, COPD, lung cancer and ALRI deaths

• Estimate mortality impacts only (not morbidity); assume scrubber will reduce deaths by ~ 72% annually

• Value reduced mortality by transferring VSL from HICs

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Mortality Impact of a Plant

• Deaths associated with a plant:

Plant deaths = (Baseline deaths) x (∆ pop-wtd PM2.5) x(∆ Mortality per μg/m3 PM2.5)

• Can measure ∆ Mortality per μg/m3 two ways:• Treat plant as marginal emitter—use slope of IER at

current annual average PM2.5• Treat plant as average emitter—use slope of a ray from

the origin to the IER at current annual average PM2.5• We treat the plant as the marginal emitter•

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Concentration-Response Function for Lung Cancer

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Deaths per 1000 tons of SO2 2015

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Mortality Benefits of Scrubbing

• Assume deaths caused by the plant grow at the rate of population growth for India

• FGD reduces deaths by 72% each year

• Number of deaths reduced each year is multiplied by the VSL for that year (see next slide)

• Monetary value of benefits are discounted to the present using three discount rates: 3%, 8%, 12%– 3% is same discount rate used in cost effectiveness reference

case– 8% and 12% reflect rate of growth of the Indian economy

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Transferring the VSL to India

• VSL transfer assumptions follow reference case guidance

• Transfer VSL from USA using an income elasticity of 1.5 and also using ratios of VSL/Y of 100:1 and 160:1

• Implies 2015 VSL in India at market exchange rates:– $84,000– $160,000– $256,000

• VSL grows with per capita income using income elasticity underlying benefits transfer

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Benefit/Cost Ratios for FGD Retrofits

Plant Name VSL $160,000 $256,000 $256,000

Discount rate 3% 3% 8%

Dadri 11 18 14Unchahar 7.5 12 9.5Bakreswar 3.4 5.5 4.3

Dahanu 2.4 3.8 3.0Talcher 1.5 2.4 1.9Koradi 1.0 1.6 1.3

Rayalaseema 0.56 0.89 0.70Tuticorin 0.51 0.82 0.65

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Issues in Calculating Net Benefits

• Only mortality benefits have been calculated– Cannot interpret B/C ratio < 1 as indicating FGD fails the benefit-

cost test– Morbidity benefits will be proportional to the size of the exposed

population and possibly to mortality– The ranking of locations by net mortality benefits should provide

useful information

• Should the VSL vary with state per capita income?– Calculations suggest prioritizing retrofits in the North and East of

India: Uttar Pradesh and Bihar are poor states

• We follow the US practice of valuing all lives the same within a country

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Per Capita Income by State (2017$)

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Conclusions

• Retrofitting coal-fired power plants with scrubbers will likely yield positive net benefits in the aggregate– This suggested by DCP3 results and VSL transfer – But wide variation in health benefits from scrubbing across

plants, due to differences in meteorology and size of the exposed population

• Information on net health benefits can aid in prioritizing retrofits– Calculations suggest prioritizing retrofits in the North and East of

India: Uttar Pradesh, Bihar, West Bengal

• Caution that benefits reflect only avoided mortality

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