The Extractive Industries and Society 2 (2015) 683–693

Original article

Coal mining in Odisha: An analysis of impacts on agriculturalproduction and human health

Padmanabha Hota*, Bhagirath BeheraDepartment of Humanities and Social Sciences, Indian Institute of Technology Kharagpur, Kharagpur, 721302 West Bengal, India

A R T I C L E I N F O

Article history:Received 3 January 2015Received in revised form 23 August 2015Available online 7 September 2015

Keywords:Coal miningAgricultureHealthOdishaIndia

A B S T R A C T

This study assesses the cost of coal mining on agriculture and human health in one of the prominentmining regions in the Indian state of Odisha. The study is based on household-level data collected fromfour mining (polluted) villages and two control (non-polluted) villages in the Ib Valley region of westernOdisha. An “effects-on-production” approach has been used to analyze the effects of pollution onagriculture, whereas a “human-capital” approach and a probit model have been applied to deriveestimates about the effects of mining on human health. The results reveal that the quantity of fertilizersused influences the average paddy yield positively, whereas the location of villages influences negativelythe same yield, implying that average yield per acre in the mining villages is significantly lower than thatof the control villages. Respiratory illness is the most prevalent and costly health problem amongindividuals residing in the area. Females are more likely to suffer from respiratory illness than males.Further, families housing greater numbers of literate persons have fewer incidence of respiratory disease.Inhabitants of the mining villages show higher exposure to respiratory diseases, than do inhabitants ofthe control villages.

ã 2015 Elsevier Ltd. All rights reserved.

Contents lists available at ScienceDirect

The Extractive Industries and Society

journal homepage: www.else vie r .com/ locat e/e xis

1. Introduction

Mineral resources play a critical role in fostering economicdevelopment, especially in developing countries such as India.These resources provide raw materials for rapid industrializationand generate a sizable amount of employment opportunities forlocal people (Mishra, 2009). The extraction of mineral resourcesalso brings other benefits to nearby communities, includingschools, hospitals, construction, and improved transportationand communication facilities. In India, the state governmentreceives revenues from the extraction of minerals, thus enablinginvestment in various welfare schemes for the enhancement of theoverall socioeconomic status of the citizenry. However, eventhough mining activities provide an impetus for economic growthand development, they are also responsible for a host of adverseimpacts, foremost degradation of the environment and naturalresources (Mishra, 2009; Li et al., 2011). Because mineral resourcesare non-renewable, their extraction has important implications forintergenerational equity. Negative externalities from extraction,such as environmental and natural resource degradation and

* Corresponding author.E-mail addresses: padmanabhahota@gmail.com (P. Hota),

bhagirath@hss.iitkgp.ernet.in (B. Behera).

http://dx.doi.org/10.1016/j.exis.2015.08.0072214-790X/ã 2015 Elsevier Ltd. All rights reserved.

depletion, are significant, which can offset the benefits from themining (Hilson, 2002). These adverse effects on agriculturalactivities (Mishra and Pujari, 2008; Li et al., 2011; Aragon andRud, 2013), human health (Hendryx and Ahern, 2008; Mishra,2010; Saha et al., 2011; Schatlez and Stewart, 2012), andecosystems (Sinha et al., 2007; Li et al., 2011) are mostly borneby local people.

Open-pit operations, especially coal mines, emit a substantialamount of dust and other particles into the atmosphere that affectother economic activities in the region, as well as human health.They increase the concentration of local pollutants in theatmosphere, such as suspended particulate matter (SPM), respira-ble suspended particulate matter (RSPM), ozone, sulfur oxides, andnitrogen oxides, which have serious implications on the health ofthe people living around the mining regions. The economic effectsof extractive industries have often been discussed in the context of“Dutch Disease,” the crowding-out effects that natural resourceabundance has on other industries, specifically increased wages,the rise in the exchange rate, and/or decreased investmentflows (Sach and Warner, 2001; Atkinson and Hamilton, 2003).However, less prominent in academic and policy debates arethe other adverse effects of mining, such as economic lossescaused by environmental degradation at the local level. Inparticular, the crowding-out effects of extractive industries onagricultural production and human health through environmental

684 P. Hota, B. Behera / The Extractive Industries and Society 2 (2015) 683–693

degradation have yet to receive much coverage in the economicliterature. Given that developing countries often have in placesubpar environmental regulations and/or enforcement, pollutionfrom mining can cause heavy economic losses to the proximatepopulation (Saha et al., 2011). The present study attempts toaddress this gap by assessing the effects of pollution from miningactivities on rural communities, in general, and on agriculturalproduction and human health, in particular, in the Indian state ofOdisha.

Although Odisha is one of the richest Indian states in terms ofmineral deposits and the mining sector contributing significantlyto the state GDP, about 60% of its population continues to dependon agriculture and ancillary sectors. Recently, the Government ofOdisha undertook various steps to promote industrialization,especially mining-based industries, which have facilitated phe-nomenal economic growth through increased foreign investments.The average contribution of mining and quarrying to Odisha’s GDPwas about 7.2% from 2004–05 to 2013–14 (Government of Odisha,2014). Growth in employment, however, has not as beenconsistent, although during the last decade, the state has had anincrease in employment in the mining sector (Government ofOdisha, 2014).

Odisha has the second largest coal deposits in India. The state,therefore, has focused largely on activities emphasizing theextraction of this coal and its use in electricity generation bythrough the construction of thermal power plants. The state is themain supplier of coal to the various thermal power stationssituated in the southern part of India. However, most of the coaldeposits are located in deep-forested areas that house a variety oftropical biodiversity and indigenous tribal populations (CSE, 2008;Mishra and Reddy, 2009). In fact, overall, the extraction ofminerals, associated activities, and rapid industrialization havecaused serious environmental problems, including air and waterpollution, degradation of cultivable land, deforestation and loss ofbiodiversity. Large tracts of forests are razed, waterways arepolluted and clogged, farmlands have been transformed into wastetracts, and dust hangs heavily in the air (CSE, 2008). The objectiveof the present study is to assess the cost of this mining-inducedpollution on agricultural production and human health. This isaccomplished using household data collected from an intensivestudy of four mining villages and two control (non-mining) villageslocated in the Ib Valley coalfield of Odisha.

The paper is divided into five sections. Section 2 presents adetailed literature review concerning the effects of pollution onagricultural production and human health. The conceptualframework for the study is then presented. A description of thestudy area, sampling method, data collection and specification ofeconometric models are discussed in Section 4. Section 5 presentsthe results and discussion. Section 6 concludes with a discussion ofrelated policy implications.

2. Literature review

2.1. Mining and agriculture

Environmental quality can be a non-market production input:damage to the environment reduces the supply of input and thusproduction falls. Conversely, improvement in environmentalquality can benefit ecologically-sensitive crops. This aspect isimportant for the developing regions of the world, such as SouthAsia, where agriculture accounts for a larger share of GDP than itdoes in higher-income regions. Most countries in South Asia areexperiencing declines in the amount of forestland, as well asdesertification and rising pollution levels. The ambient air andwater pollution in South Asia on average is much higher than inmost other areas of the world. A study by the World Bank (2013)

has estimated that in India, every year, environmental degradation,largely caused by the burning of fossil fuels, as well as a lack ofaccess to clean water supply, sanitation, and hygiene, are costingaround 5.7% of India’s GDP. In a similar study by the World Bank(2007), it was estimated that the total cost of air and waterpollution in China is about 5.8% of that nation's GDP.

Air pollution affects agriculture in numerous ways. It has thepotential to reduce both the yield and the nutritional quality ofcrop plants. It adversely affects plants by either reducing yields ordegrading the quality of agricultural product (Spash, 1997).Therefore, agricultural production would fall even if there wereno change in the quantity of other inputs used. The resultingeconomic losses from air pollution have become an issue ofpolitical and scientific concern in many areas worldwide. The airpollutants that are most damaging to agriculture are gaseoussulfur and nitrogen compounds, photochemical oxidants, ozone,and SPM. Ambient air pollution has been shown to reduce thegrowth and economic yield of a wide range of major crop speciesin North America and Europe (Holland et al., 2006). Such effectsare currently attributed largely to the secondary photo-oxidantozone (O3), which is widespread in many rural areas. Theformation of ozone is influenced by major emissions of itsprecursors, nitrogen oxides (NO) and hydrocarbons, for which,the motor vehicle is the greatest source. However, in some areas,nitrogen dioxide (NO2), sulfur dioxide (SO2), and other airpollutants are also important in terms of crop yield (Skinneret al., 1997). The synergistic effects of ozone and SO2 make airpollution even more threatening to agriculture (Chen et al., 1996cited in Wei et al., 2014).

There have been a number of empirical studies on agriculture-related environmental problems such as soil degradation andwind and water erosion (Lin et al., 2013; Merten and Minella,2013; El Azab et al., 2015), but only a few studies have beenfocused on environmental problems in the agricultural sectorcaused by mining pollution. Through the estimation of anagricultural production function using household-level data,Aragon and Rud (2013) found that nitrogen dioxide pollutionfrom gold mining in Ghana has reduced agricultural productivityby 40%, between 1998 and 2005 and further, that the negativeeffects decline with distance and extend to areas within 20 kmfrom mine sites. In mining regions, the presence of high levels ofsuspended particulate matter is a major problem for agriculture.It is observed that when coal dust falls onto the plants it affectstheir nutrients, photosynthesis and production (Li et al., 2011).Using the Fisher and Tornqvist indices of Total Factor Productivity,Mishra and Pujari (2008) have shown that villages located nearcoal mines have suffered from a loss of productivity in ricecultivation because of the high presence of coal dust. Sulfur incoal dust affects the respiration of crops. If it reacts with dew andrainwater, it produces an acidic compound that burns crop laminaand reduces crop outputs (Li et al., 2011). Wei et al. (2014)attempted to estimate the effect of sulfur dioxide (SO2) pollutionon agricultural development, carrying out their study in899 heavily polluted Chinese counties. The cost of agriculturallosses resulting from SO2 emissions was estimated at 0.66% of thetotal agricultural value added in those regions. In addition to aloss in productivity, pollution makes the products unsalable orsalable at a lower rate (Behera and Reddy, 2002; Reddy andBehera, 2006).

2.2. Mining and human health

Health can be defined as a state of complete physical, mental,and social well-being of an individual, and not merely the absenceof diseases (WHO, 2009). Environmental pollution affects humanwell-being in various ways. Although a clean environment is

P. Hota, B. Behera / The Extractive Industries and Society 2 (2015) 683–693 685

considered essential for human health and well-being, economicdevelopment has resulted in a considerable deterioration inenvironmental quality across the globe. The effects of environ-mental pollution come in the form of externalities. Therefore,the cost of pollution is not reflected by the market but ratherinflicted upon society. Air pollution can lead to serious publichealth problems, including acute respiratory illness and chronicbronchitis, and possibly premature death for more vulnerablepopulations (Zhang et al., 2010). Currently, air pollution isconsidered the world's largest single environmental health risk(WHO, 2014). Globally, the majority of premature deathsand morbidity occur because of both ambient and indoor airpollution (Zhang et al., 2010; Lim et al., 2012). A recent study bythe WHO (2014) reports that around 7 million people died in2012 because of air pollution.

The populations of developing countries suffer more healthproblems associated with environmental degradation, comparedwith residents of developed nations (Pearce, 1996). Environmentalhazards, such as water contamination, air pollution and unhygienicconditions are largely responsible for both morbidity and mortalityin developing countries. Indoor air pollution is also an importantcontributor to the global burden of disease. Globally, 4.3 milliondeaths were attributable to household air pollution in 2012, mostof which occurred in developing countries (WHO, 2014).

A number of studies about the link between pollution andhealth-related social costs in developed countries have beenconducted. Because of the shortage of original studies indeveloping countries, researchers have often been forced toextrapolate concentration-response functions estimated in thelikes of the United States to the levels of pollution in the targetcountry (Ostro, 1994). However, this approach has been criticizedbecause of the different cultural, behavioral, and institutionalcircumstances, and the difficulty of finding places with matchingenvironmental conditions from which predictions can be made forthe target country and, therefore, may yield misleading results(Alberini and Krupnik, 2000; Gupta, 2011). Thus, to obtainestimates that are more accurate of welfare gains from reductionin pollution, detailed area-specific studies are required.

EmpOpp

Pub

Infra

Loss Land in pr

DamPoll ubod i

Loss Poll u

Loss and B

Impac t of coal Mining

Positive Impac ts

NegativeImpac ts

Fig. 1. Pathways of impact of coal mining

The majority of health problems in mining regions are causedby air and water pollution and accidents at sites. The land, bodiesof water, air, and environment become contaminated fromreleases of chemical waste, dust generated by blasting andexcavation, and the dumping of mine waste (CMRI, 1998). Sourcesof air pollution in coal mining areas generally include drilling,blasting, overburden loading and unloading, coal loading andunloading, road transport, and losses from exposed overburdendumps, coal handling plants, exposed pit faces and workshops(CMRI, 1998). The health and safety problems vary from onemineral to the other, and according to the type of technologyused, type of mines, and the size of operations. Because theconcentration of particulate matter is high in mining areas,respiratory diseases are more common in mining communities(Saha et al., 2011). Persons living in communities near tounderground mines may suffer similar health risks from pollutionto those living near to opencast mines because of transportand associated activities (Mishra, 2010). With increasing mecha-nization of mines, more fine dust is being generated, which isproving to be hazardous to human health.

Mining remains one of the most hazardous occupations in theworld, both in terms of short-term injuries and fatalities and long-term human health problems from diseases such as cancer andrespiratory conditions (Stephens and Ahern, 2001). The evidenceindicates that people residing in coal mining communities are at anincreased risk of developing heart and lung diseases, cancer,hypertensions, and kidney diseases, and mortality rates are higherin communities located in closer proximity to coal mines and coal-fired power stations (Hendryx et al., 2008; Hendryx and Ahern,2008). Moreover, people who live close to mining areas are likely toconsume water laced with chemical waste and debris from miningactivities. Occupational diseases such as silicosis and coal workerspneumoconiosis (CWP) occur in underground coal mine workers,induced by the inhalation of mine dust (Schatlez and Stewart,2012). Apart from chemical hazards, high temperatures andhumidity may cause heat-related illness; long-term exposure ofmineworkers to heat stress renders them less productive (MSHA,2007; Maurya et al., 2015).

Schools

Hospitals

Transportation

Loss of Human health

Loss of Agricultural Outputs

Loss of Traditional Liveli hoods

loyment ortun ities

lic Go ods

structure

of Cultivable s and decline odu ctivity

age and tion of Water

es

du e to Air tion

of Forests iod iversity

on local economy and environment.

Table 1Key features of sample villages.

Features Mining villages Control villages

Kirarama Ubuda Kandasor Tingismal Kutrapali Jharupada

Distance fromnearby mines (inkm)

3 0.5 2 0.5 13 23

Total population 902 1223 174 826 636 899Social composition Mixed Mixed Mixed Mixed Mixed MixedTotal households 130 216 42 202 90 180Occupation Mine's employee,

agriculture and casuallabor

Mine's employee,agriculture and casuallabor

Mine's employee,agriculture and casuallabor

Mine's employee,agriculture and casuallabor

agriculture andcasual labor

agriculture andcasual labor

Source: Field Study.

686 P. Hota, B. Behera / The Extractive Industries and Society 2 (2015) 683–693

3. Conceptual framework

Mining in environmentally sensitive regions is likely to affectecosystem services that are vital to the sustenance of local humanlivelihoods. As mentioned previously, mining activities are carriedout inside of forests that local people depend on for theirlivelihoods. People derive a variety of benefits from forests, suchas timber, non-timber forest products (NTFPs), watershed protec-tion, and others. Mining can adversely affect access these benefits.Exposure to pollution from mines can affect the health of localpeople, which in turn can reduce their income and the overallwelfare of families.

Fig. 1 presents a framework of how mining activities affect thelocal economy and the environment in both a positive and negativedirection. Mining is a major economic activity that affects theregional and local economy significantly. It not only creates directand indirect employment opportunities but also catalyzes theconstruction of basic infrastructural facilities such as roads,schools, hospitals, and transportation in remote areas (Hajkowiczet al., 2011). Moreover, it generates a significant amount of revenuefor government, which enables it to finance various welfareschemes aimed at improving the living standards of people.However, extraction of minerals also results in degradation of theenvironment that can have adverse effects on the livelihoods oflocal people (Kitula, 2006; Sinha et al., 2007; Mishra, 2009).

In India, most mineral deposits are located in ecologicallysensitive forested areas. Their extraction disturbs complexecosystems. Studies have confirmed that mining has had animpact on the health of communities located around mines (Sahaet al., 2011; Shandro et al., 2011) as well as on agriculturalproduction (Mishra and Pujari, 2008; Li et al., 2011; Aragon andRud, 2013).

4. Study area, data collection and methodology

4.1. Description of the Study-Area

The study draws on the results of fieldwork conducted in theLakhanpur area of the Ib Valley1 coalfield in Jharsuguda district,Odisha. The Ib Valley coalfield is situated between latitudes 21�3200

to 22�0600N and longitudes 83�3200 to 84�1000E, covering an area of1375 km2. According to the Geological Survey of India, the Ib Valleycoalfield has reserves of 22.3 billion tons of coal, the third largest inIndia. In this coalfield, mining is carried out by Mahanadi CoalfieldLimited (MCL), a subsidiary of Coal India Limited (CIL). There arethree major mining areas in Jharsuguda district. These are theOrient area, Ib Valley area and Lakhanpur area. The latter is

1 The coalfield is named after the River Ib, a tributary of the River Mahanadi.

relatively new to coal exploration, compared to the others. Theselection of control villages was a major concern because theOrient and Ib Valley areas have been polluted by mining andassociated activities for over a century. Lakhanpur, therefore, wasselected as the study area, in order to identify appropriate controlvillages with similar socioeconomic and geographic conditions andwhich were not impacted by pollution caused by either mining orthermal power plants. Notably, Lakhanpur has one thermal powerplant that is in operation at Banharpali; another is underconstruction at Sahajbahal. All of the mining and control villagesbelong to the same administrative block.

This area has three opencast mines: Lakhanpur, Belpahar, andLilari. The selection of the study region is justified on the followinggrounds. First, with improved technology, the mining sector hasbecome a capital-intensive and heavily mechanized industry. Asmentioned, coal mining has become the most important extractiveindustry in Odisha. Moreover, most of the mines are located in aheavily-forested region of the state, where they have adverselyaffected the local ecosystem. This has had serious consequences onthe long-term sustainability of the local economy. Second, minesare located in rural areas, where agriculture and allied activities arethe main sources of livelihoods for local people. Extraction of coalin such a region, therefore, is likely to affect the local economyadversely.

Studies in the region indicate that mining and the transporta-tion of coal in the Ib Valley have adversely affected the atmosphericenvironment of the region, and that the local inhabitants areconstantly being exposed to pollution (Mohapatra et al., 2010).Some other studies on air pollution in the Ib Valley coalfield havealso shown that the SPM and RSPM concentration exceeds theNational Ambient Air Quality Standards (NAAQS) for both miningand residential areas (CSE, 2008; Chaulya, 2004).

4.2. Sampling method and data collection

The data and other related information were collected using aprimary survey conducted during the period of June–September2013. Generally, “impact assessment” studies tend to feature a“double difference” method, in which both a “before-and-after”and “with-and-without” approach are employed (Reddy andBehera, 2006). The usual problem associated with the before-and-after approach is memory lapse, if a long time gap exists. Inour case, because mining in this area commenced 30 years prior, itwas inappropriate to use a before-and-after approach becausepersons might have found it difficult to recall information onmatters that happened 30 years ago. Therefore, the study wascarried out using the with-and-without method. Mining villagesare located near sites, and the households within these villages areclosely clustered. All of the villages were in existence in theircurrent location before mines commenced operation and,

Table 2Average household earnings from traditional livelihood sources (in INR).

Villages Agriculture NTFP

P. Hota, B. Behera / The Extractive Industries and Society 2 (2015) 683–693 687

therefore, households' residential choices are not determined byexposure to operations. Thus, for all practical purposes, it isassumed that all individuals within a village face the sameenvironmental pollution from mines. However, people working inmines have more environmental and occupational exposure topollution. The sample consists of four villages situated within 5 kmof an active mining zone (mining villages) and two villages beyond5 km from an active mining zone (control villages) but belonging tothe same administrative block and agro-climate zone as that of theJharsuguda district. The socioeconomic conditions, traditionalsources of livelihoods and cropping patterns are similar in both thecontrol and mining villages. The major interzonal differences arethe availability of employment opportunities at mines andenvironmental pollution. Household data from the mining villagescan be compared with the data of control villages to infer if thereare any possible environmental externalities. From the discussionswith villagers, it was found that in both mining and controlvillages, agriculture was the main source of livelihood. Irrigationintensity in both mining and control villages was the same becauseall villages are in “unirrigated area,” and rainfall is the only sourceof irrigation. Households in mining villages also reported thataverage yield was similar between the villages before miningoperations. It was only during the 1990s that the villagers hadnoticed a decline in agricultural productivity, which in their view,was due to the mining that had commenced at this time. Eventhough local people pursued mining jobs, households in miningvillages continued to engaged mainly in agricultural activities.Negligence, therefore, was not be the reason behind the decline inagricultural output in these mining villages. Table 1 presents thekey features of the sample villages.

Data from individual households were gathered using astructured questionnaire designed to elicit information ondemographics, and peoples' occupations, health outcomes andaccess to public facilities. In total, 295 households were randomlysampled, 205 from the mining villages and 90 from the controlvillages. The sample included data on 1474 individuals from295 sampled households. Occurrences of various diseases amongindividuals during the period from May 2013 to August 2013 wereselected for our analysis. The effect of mining pollution onagricultural production was assessed using data on the paddycultivation of the sampled households, since rice is the principalcrop grown in the region. We used the data from 152 plots (i.e.,94 plots in the polluted villages and 58 plots in the non-pollutedsample villages). Data on input use, cost and returns from paddycultivation were collected and used to analyze the effects of miningon paddy production. Data pertaining to paddy cultivation are forthe year 2012, during Kharif season (June to December). Paddyproduction, fertilizer, and farmyard manure are measured inkilograms. The variable machinery cost is measured as tractorhours. Some farmers were using power tillers to plow, and we haveconverted their activity into a tractor-hours equivalent.2 Labor isexpressed in days, and it consists of own and hired labor. Bullocklabor is also expressed in days, and it consists of own and hiredbullock labor.

Before mining began, the Ib Valley region was underdeveloped,with agriculture and allied activities comprising the main sourcesof livelihood and employment for the local people. Large depositsof coal and the proximity to the Hirakud reservoir have made thisarea one of the most attractive and globally competitivedestinations for mineral-based industries (OSPCB, 2010). A numberof sponge iron plants, thermal power plants, aluminum smelters,and iron and steel plants have opened in the region in the past few

2 This is done according to how many tractor hours can be obtained from the totalcost incurred in plowing with a power tiller.

years. A significant mobility of labor, from agricultural activities tonon-agricultural activities, therefore, has been observed in themining villages. It was observed that along with cultivation, mostof the villagers work as casual laborers. In recent years, a shortageof field labor in both the mining and non-mining villages hasappeared because of government schemes which supply ricecheaply at one rupees/kg, the National Rural EmploymentGuarantee Act (NREGA) and fewer school dropouts. Manyagricultural laborers work on a contractual basis (i.e., mostly onone-year contracts under farmers) but because rice is subsidized,many residents prefer to be employed as casual laborers. Accordingto many of the villagers consulted, the cost of production hasincreased significantly because of this reduction in labor supply,but at the time of writing, farmers had not encountered any majorproblems with their agricultural activities or any consequentreduction in yield.

Table 2 provides a comparison of mean household earningsfrom traditional sources of livelihood. Income from traditionalsources such as agriculture and NTFP are significantly lower inmining villages than control villages. In both mining and controlvillages, many households are engaged in vegetable cultivation.Households reported that vegetable farming is an importantsource of income because earning from paddy cultivation isinsufficient for sustaining their livelihoods. The residents of manyhouseholds in the mining villages collect varieties of NTFPs, such astendu, mahua, mushroom, and fruits, in the nearby forest.Households also collect mahua from their own lands, which area part of their traditional livelihood systems. Many intervieweesreported that their ancestors had planted trees on their land, fromwhich they collect the mahua flower. But several of the trees havesince either been uprooted because of land acquisition for miningor removed due to their growth being stunted by pollution. Therewere claims made that several of the remaining trees are notflowering properly because of dust from mining and transportvehicles, and that yields have declined. Households in Kandasorand Tingismal are engaged more in the collection of NTFPs becausemost residents do not have jobs. In control villages, environmentalquality is reportedly good; efforts aimed at collecting NTFPs havenot been disturbed; and people continue to generate significantamounts of income from the sale of collected product.

4.3. Analytical method

In addition to descriptive statistics, two sets of analytical(econometric) models were used to analyze the data. The firstmodel (production function) was used to identify and analyze thefactors that influence the paddy yield. The second model (probitmodel) was used to identify the factors that influence theoccurrence of respiratory illness. Detailed specifications of thesemodels and variable descriptions, and related hypotheses, arepresented below. The statistical package Stata was used to analyzethe data.

4.3.1. Estimating the effects of mine-related pollution on agriculturalproduction (paddy yield)

The effects-on-production approach can help to explain therelationship between environmental attributes and the outputlevel of an economic activity. In this method, along with

Mining villages 7735.85 1131.70Control villages 17259.44 4358.33t-test �3.63*** �4.45***

Note: *** = significant at 1%.Sources: Field Study.

688 P. Hota, B. Behera / The Extractive Industries and Society 2 (2015) 683–693

conventional inputs, the quality of the environment is alsoincluded as an input in the production process. Damage to theenvironment reduces the supply of these inputs, and as a result,production falls. Conversely, programs to improve environmentalquality can benefit environmentally sensitive forms of productionby raising the supply of such inputs (Vincent, 2011). If there is amarket for goods and services, the effects of environmental effectcan be represented by the value of the change in output.Environmental quality can be interpreted as an input in a firm'sproduction-function analysis and the resulting economic out-comes can be measured by looking at the effect of pollution onagricultural production, and valuing this effect using market prices(Behera and Reddy, 2002; Reddy and Behera, 2006). Theproduction-function approach is often used to estimate the effectof environmental change on soil erosion, deforestation, fisheries,and the effect of air and water pollution on agriculture (Batemanet al., 2003).

A generalized neoclassical production function can be used forestimating the productivity changes in agriculture due to pollutionas follows:

Y ¼ f ðL; K; M; QÞ; ð1Þwhere Y is the paddy yield of a farmer in the studied year (kg/acre);L is the labor cost for paddy cultivation (INR/acre); K is machinerycost (INR/acre); M is a vector for material inputs such as seeds,fertilizers, and pesticides; and Q is the relative location of the firm(polluted site = 1, non-polluted site = 0).

Production with a higher level of environmental quality ishigher than one with a lower level of environmental qualitybecause it is assumed to increase the function of environmentalquality. Therefore,

@Y@Q

ðL; K; M; QÞ � 0: ð2Þ

In our study, the effect of mining pollution on agriculture hasbeen estimated using current market prices. It is assumed thatbesides physical inputs, environmental quality is likely to influencepaddy yield. The dependent variable for this study is yield per acre,whereas the explanatory variables include, input use, such ashuman labor and machinery cost as a percentage of total cost;quantity of seeds, fertilizer, and pesticide; and the location of farm.Detailed definitions and descriptive statistics are presented inTables 3 and 4, respectively.

To assess the effect of mining pollution on paddy yield, thefollowing paddy production function was used:

Y ¼ aXb11 Xb2

2 Xb33 Xb4

4 eu ð3ÞFurther, to estimate the production function, pooled regressions

of paddy yield in both mining and control villages on the inputvariables were used. In the regression, yield is measured askilogram (kg) of paddy per acre. For labor input, wages are thesame for a particular operation on all farms in a village. Thus, per-acre labor costs are calculated and used in the models. In the case ofmachinery, per-hour unit cost of machinery for plowing is thesame for all firms in a village. Accordingly, total machinery cost per

Table 3Description and measurement of the variables for agricultural production function.

Variable Definition

Y Quantity of paddy (kg/acre)X1 (Human labor cost + machinery cost)/total costX2 Quantity of seeds (kg/acre)X3 Quantity of fertilizer (kg/acre)X4 Quantity pesticide (ml/acre)D Village dummy (1 = mining village; 0 otherwise)

acre was calculated based on the number of hours that machinerywas used.

4.3.2. Estimating the effects of mining pollution on human healthTo identify and analyze the factors affecting human health, a

human-capital approach and a probit model were used. The formerconsiders human life as a “piece” of productive capital and theearnings as returns to investment (Reddy and Behera, 2006).Environmental economics focuses on the effect of poor environ-mental conditions on human health, as well as the resulting effecton the individual's and society's productive potential (Batemanet al., 2003). In our study, we have calculated the economic cost ofthe illness of a productive human being. The cost of an illnessincludes loss in work income and medical costs incurred to relievethe illness. However, we have included in the cost, the help ofmedical science or epidemiological data to correlate pollution.Nevertheless, from the secondary sources, we have evidence thatthe ambient pollution is above the safe standards in the studyregions (Chaulya, 2004; CSE, 2008; Mohapatra, 2010; OSPCB,2010), which could lead to various types of diseases. Ourdiscussions with local doctors also confirmed the positiverelationship between pollution and prevailing diseases in thevillages. The probit model has been applied to identify and analyzethe factors that influence the occurrence of respiratory illness.Respiratory illness includes both upper acute respiratory illness(URI) and lower acute respiratory illness (LRI).

The probit model is used for identifying and analyzing thefactors that influence the occurrence of respiratory illness. Themodel can be expressed as follows:

Y�i ¼ bXi þ ui: ð4Þ

Here, Y* is the unobserved dependent variable (commonly knownas the latent variable), b is a (K � 1) vector of unknown parameters,X is a (1 � K) vector of socioeconomic and institutional factors, andu is the random disturbance term that is independently andnormally distributed with mean 0 and variance s2. It is assumedthat one cannot observe Y*; instead, a person can only observe adummy variable defined as follows:

Yi ¼ 1 if Y�i > 0

0; otherwise

�

The dependent variable is Y = 1 (i.e., if any individual in thefamily was affected by respiratory illness). However, if Y = 0, then Y*

does not exist.The dependent variable in the model is binary in nature. If an

individual is reported to have had a respiratory illness, the value ofthe dependent variable is 1 and otherwise zero. The explanatoryvariables include household characteristics and social andinstitutional factors, such as number of adult literates in thefamily, caste background, family income, age, gender andoccupation. A detailed definition, as well as the expected directionof effects, is provided in Table 5. Summary statistics of the variablesused are provided in Tables 6 and 7.

The gender of the individual is hypothesized to affect theoccurrence of respiratory illness. Studies have found that femalesare more affected by diseases relating to pollution than males(WHO, 2014). This is because, besides outdoor pollution, femalesexperience higher exposure to indoor pollution than males. Age ishypothesized to influence the prevalence of respiratory illness.Children and elders usually experience low immune responses toenvironmental exposure, leading to increased susceptibility todiseases (Bogahawatte and Herath, 2011). Social backgroundrepresented by caste is hypothesized to have an effect on theoccurrence of respiratory diseases. There is evidence that ethnicminorities suffer more from pollution-led diseases than do others

Table 4Descriptive statistics of agricultural inputs and output.

Variables Mining villages Control villages Combined

Obs. Mean SD. Obs. Mean SD. Obs. Mean SD.

Yield (kg/acre) 94 960.73 374.23 58 1384.76 461.99 152 1122.53 435.81Labour cost (INR/acre) 94 6568.28 1194.13 58 6291.13 1224.93 152 6462.52 1209.50Machinery cost (INR/acre) 94 970.59 503.16 58 1072.23 419.20 152 1009.37 474.04Seeds (kg/acre) 94 64.82 9.05 58 58.21 9.93 152 62.30 9.90Fertilizer(kg/acre) 94 107.53 31.82 58 87.36 46.50 152 99.83 39.20Pesticide (ml/acre) 94 146.56 60.49 58 162.57 90.53 152 152.67 73.54

Source: Field Study.

Table 5Description of variables included in the econometric analysis of determinant of respiratory disease.

Variables Description Expected sign

Gender Male = 1, female = 0 �Age Age = 1, for individuals when age is higher than 55 years or less than 15 years; others = 0 +Education Number of adult literates in the family �Lnincome Natural logarithm of family income �Minejob Occupation, working in mine = 1, others = 0 +Caste Social status, SC/ST = 1, others = 0 +Location Village dummy, mining village = 1, others = 0 +

Table 6Summary statistics of variable included in the econometric analysis of determinantof respiratory disease (all villages combined).

Variables Observation Mean Std. Dev. Min Max

Gender 1474 0.53 0.49 0 1Age 1474 0.37 0.48 0 1Education 1474 3.27 1.85 0 10Caste 1474 0.56 0.49 0 1Lnincome 1474 4.39 0.88 2.71 6.87Location 1474 0.73 0.44 0 1

Table 7Summary statistics of variable included in the econometric analysis of determinantof respiratory disease for mining villages.

Variables Observation Mean Std. Dev. Min Max

Gender 1082 0.534 0.499 0 1Age 1082 0.352 0.478 0 1Education 1082 3.601 1.918 0 10Caste 1082 0.547 0.498 0 1Lnincome 1082 4.61 0.965 2.667 6.867Minejob 1082 0.167 0.373 0 1

Table 8Mean comparison of paddy production for mining and control villages.

Sl No Items Mining v

Mean

1 Seeds (kg/acre) 64.82

2 Organic manure (kg/acre) 1030.13

3 Fertilizer (kg/acre) 107.53

4 Plant protection (ml/acre) 146.56

5 Labor cost (Rs/Acre) 6568.28

6 Bullock labor (day/Acre) 7.29

7 Machinery (Rs/Acre) 970.59

8 Productivity of paddy (kg/acre) 960.73

9 Net Return with own labor cost (Rs/Acre) �428.28

10 Net Return without own labor cost (Rs/Acre) 3318.58

Notes: H0: Mean (Mining villages) � Mean (Control Villages) = 0.H1: Mean (Mining villages) � Mean (Control Villages) 6¼ 0.***, **, * denote statistically significant at 1%, 5% and 10% level, respectively.Source: Fie

P. Hota, B. Behera / The Extractive Industries and Society 2 (2015) 683–693 689

in a society (Clougherty and Kubzansky, 2009). Level of income isassumed to be positively related to the good health conditions ofpeople as individuals with higher income tend to enjoy betterstandards of living, as well as access to better health care andsanitation facilities. A village dummy is hypothesized to have apositive effect on incidence of respiratory illness, as studies showthat environmental exposure decreases with an increase indistance from a pollution source (Hendryx and Ahern, 2008; Sahaet al., 2011). The variable job in a mine is hypothesized to have apositive effect on prevalence of respiratory illness as peopleworking in mines are expected to be exposed to more pollutionand, thus, are more vulnerable to diseases (Saha et al., 2011).

5. Results and discussion

5.1. Effects of mine-related pollution on agricultural production

Table 8 presents summary statistics of inputs used in paddycultivation, outputs, and net returns of the mining and controlvillages. Net returns per acre in mining villages (Rs. �428.28) areless with more variations than those of control villages(Rs. 5732.46), which illustrates the main difference between the

illages Control villages t-test

CV (%) Mean CV (%)

13.96 58.21 17.05 4.12***

106.34 868.80 177.54 0.6929.60 87.37 53.23 2.90***

41.27 162.57 55.69 �1.1918.18 6291.13 19.47 1.3662.45 5.04 57.80 3.72***

51.84 1072.23 39.09 �1.3438.31 1384.76 29.68 �6.42***

926 5732.46 94.53 �7.51***

123.87 9649.62 50.37 �8.26***

ld Study.

690 P. Hota, B. Behera / The Extractive Industries and Society 2 (2015) 683–693

paddy economics of the villages. Many farmers are incurring netlosses in mining villages because of low yield, if their own laborcost is considered. However, there are net positive returns ifindividual labor cost is not considered, as shown in the table.Mining villages are found to use more seeds than control villages.However, for organic manure and plant protection, we did not findany significant difference between the mining and control villages.There are significant differences in the use of fertilizers betweenthe control and mining villages. In terms of labor cost per acre, wedid not find any significant difference between the mining andcontrol villages. The difference in machinery cost per acre is alsofound to be insignificant between the mining and control villages,although the former were found to use more bullock labor. Hence,overall we conclude that mining villages use more inputs thancontrol villages do. This may be due to the higher average incomeof households in mining villages compared with that of non-mining villages because of more employment opportunities, bothdirect and indirect, due to the presence of mining and associatedactivities. Our results show that there is a significant difference inthe yields between them. This could be due to differences inenvironmental quality between the mining and control villages.

Table 9 reports the regression results of the paddy productionfunction. Because this is a multiple regression model, the varianceinflation factor (VIF) for each of the independent variables wascomputed. It was observed that the VIF for all of the variables is lessthan 2, implying that the estimated model does not suffer from asevere multicollinearity problem. The elasticity of output withrespect to fertilizer is positive and significant at 1%. This impliesthat greater use of fertilizer is associated with more paddy yield.The village dummy is also significant at 1%, which suggests thatenvironmental quality is adversely affecting paddy yield in themining villages.

5.2. Effects of mining pollution on human health

People in sample villages were found to be suffering fromvarious types of disease caused by air and water pollution. It wasobserved that some of the diseases are water-borne, including skindiseases and joint pain. Nevertheless, the majority of them areairborne, such as respiratory diseases, tuberculosis (TB), cough andcold, gastric problems, and eye problems. According to the localdoctors, the common mine-related diseases observed in the areaover the years include, but are not Limited to, vector-bornediseases such as malaria; respiratory tract diseases, especially TB;skin diseases; and eye diseases, especially acute conjunctivitis.

There was an increase in respiratory diseases and conjunctivitisreported in the area. This has been attributed to the high incidenceof particulate matter in the area. Interviewees complained of highdust content and accuse the mining company of inadequate dust-controlling measures. Malaria is a major public health problem inthe area. Most of the communities contacted complained that the

Table 9Estimates of production function for paddy farms.

Dependent Variable LnY Independent variables Coefficient

LnX1 �0.384

LnX2 0.214

LnX3 0.147

LnX4 0.077

D �0.469

Constant 5.150

R2 0.307F 15.29***

N 152

Note: ***, **, * denote statistically significant at 1%, 5% and 10% level respectively, Y =X2 = Quantity of seeds (kg/acre), X3= quantity of fertilizer (kg/acre), X4= quantity pestic

concentration of mining operations is responsible for the highincidence of malaria in the area: mining activities have changedenvironmental conditions that have attracted mosquitoes andincreased occurrences of malaria, it was explained. Specifically,mining activities create open pits, which contain bodies ofstagnant water that have been instrumental in furthering thespread of malaria. Cases of skin diseases were observed to beprevalent in communities that were visited. People blamed thehigh incidence of skin disease on the activities of the miningcompanies. They alleged that mine runoff and coal dust in pondsand wells pollute the water that they use for bathing and drinking.

The major health problems reported included fever, gastritis,joint pain, eye irritation, and skin diseases (Table 10). Of these,respiratory illness was the most prevalent. About 36% ofindividuals surveyed in mining villages reported having sufferedfrom respiratory illness, compared to only 25% of individuals in thenon-polluted villages. The second major complaint raised wasgastritis disorder: 7.39% of those surveyed in mining villages and3.06% surveyed in control villages. Joint pain is another healthproblem; in each village, households complained about it. Skindiseases and eye irritation are two other common diseases in thepolluted area. Although MCL provides free medical facilities, theseare restricted to employees and family members, and are Limitedin the sense that, in many of the cases, the doctors are referring toother hospitals for treatment because the health center there lacksproper facilities.

The average cost of health per individual is calculated on thebasis of working days lost because of illness and the medicalexpenditures incurred for treatment. Respiratory illness was themost costly disease reported (Rs. 514.53/person). Because ofpollution, the prevalence of respiratory illness is higher and henceeconomic loss is greater. Cost incurred because of the presence ofwater-borne diseases such as joint pain and diarrhea was very highamong individuals in the study group.

5.3. Determinants of factors affecting respiratory illness

Table 11 presents the results of the probit (as well as logit)model of factors affecting the occurrence of respiratory illnessamong the individuals. Overall, the model is statistically significantat 1%. The coefficient of gender is negative and significant, whichshows that females are more vulnerable to respiratory illness. Thismay be because women perform most of the household work andcooking and are thus exposed to more indoor pollution thanothers. Education is negative and significant, suggesting thathouseholds with a greater number of literate adults are lessaffected by the occurrence of respiratory illness. This may bebecause educated households are more aware of the health-relatedconsequences of environmental pollution and are making preven-tive efforts. The variable caste is positively associated withrespiratory illness, which shows that people with lower social

Standard Robust error T-statistic VIF

0.242 �1.58 1.430.175 1.22 1.200.080 1.84* 1.360.061 1.27 1.170.056 �8.25*** 1.330.731 7.04***

quantity of paddy (kg/acre), X1= (human labor cost + machinery cost)/Total cost,ide (ml/acre), D = village dummy (1 = mining village; 0 otherwise).

Table 10Major health problems among individuals.

Total health problems Mining villages (N = 1082) Control villages (N = 392)

% of individuals reported Cost/person(INR) % of individuals reported Cost/person(INR)

Respiratory illness 35.76 514.53 25 182.15Joint pain 5.54 66.12 4.33 33.18Gastric 7.39 85.77 3.06 53.57Conjunctivitis 6.56 13.75 3.06 3.18Malaria 5.92 73.98 3.31 41.04Skin diseases 5.82 14.58 2.29 4.69Dehydration 5.26 70.14 4.33 20.48Stomach 4.8 80.31 5.86 31.88

Sources: Field Study.

Table 11Probit/logit model for the occurrence of respiratory illness (all villages combined).

Independent variables Coefficient Robust standard error Z

Probit Logit Probit Logit Probit Logit

Gender �0.157 �0.257 0.068 0.112 �2.30** �2.28**

Age 0.004 0.006 0.071 0.116 0.06 0.05Education �0.104 �0.172 0.023 0.038 �4.51*** �4.42***

Caste 0.133 0.214 0.070 0.114 1.93* 1.88*

Lnincome 0.123 0.203 0.047 0.077 2.63** 2.63**

Location 0.376 0.616 0.083 0.139 4.49*** 4.41***

Constant �0.929 �1.509 0.187 0.309 �4.95 �4.88Log pseudolikelihood �909.601 �909.773Prob > chi2 0.0000 0.0000N 1474 1474

Note: *** = Statistically significant at 1%, ** = statistically significant at 5%.

Table 12Probit/Logit model for the occurrence of respiratory illness in polluted villages.

Independent variables Coefficient Robust Standard error Z

Probit Logit Probit Logit Probit Logit

Gender �0.651 �1.081 0.092 0.157 �7.06*** �6.87***

Age 0.223 0.355 0.090 0.149 2.51** 2.38**

Education �0.087 �0.145 0.025 0.043 �3.37*** �3.37***

Caste 0.051 0.070 0.082 0.137 0.62 0.51Lnincome 0.052 0.092 0.051 0.086 1.02 1.07Minejob 1.392 2.277 0.126 0.213 11.00*** 10.67***

Constant �0.326 �0.530 0.216 0.360 �1.51 �1.47Log pseudolikelihood �633.688 �633.170Prob > chi2 0.0000 0.0000N 1082 1082

Note: *** = Statistically significant at 1%, ** = statistically significant at 5%.

P. Hota, B. Behera / The Extractive Industries and Society 2 (2015) 683–693 691

backgrounds are most often affected by respiratory illness. Thismay be because lower-caste people have relatively little interac-tion with others and thus lack knowledge of environmentalexposure and disease, as well as necessary precautions. The villagedummy is positive, which shows that individuals in the miningvillages have more respiratory illness than those in control villages.

Further, both the logit and probit models for individuals in themining villages are significant at 1%, as shown in Table 12. Thevariable gender is positive and significant, suggesting that femalesin mining villages have more respiratory illness. Education isnegative and significant, indicating that literate adults are lessaffected by the occurrence of respiratory illness. Caste is positive,which suggests that more people with lower social backgroundare affected by respiratory illness. The variable job in mines ispositive and significant, indicating that individuals working inmines are more affected by respiratory illness than those inpolluted villages because of environmental and work-placeexposure to pollution.

A larger proportion of people in the polluted villages aged lessthan 15 and those above 55 are affected by respiratory illness, thanthose in the same age range in control villages. Households inpolluted villages have better average annual income than those incontrol villages. This is largely due to the higher wages earned inmining. For the combined model, income has a positive effect onthe occurrence of respiratory illness. In the mining villages,individuals are affected by pollution irrespective of their incomelevels, and thus suffer from respiratory illness. Therefore, incomematters significantly overall, but this is not the case in pollutedvillages alone.

6. Conclusion

Our analysis provides evidence of the negative effects of miningactivities on local communities in the Ib Valley coal mining regionin the Jharsuguda district of Odisha. These activities have adverselyaffected agriculture and human health. Notably, in economic

692 P. Hota, B. Behera / The Extractive Industries and Society 2 (2015) 683–693

terms, the cost incurred by the local communities in terms of lossof agricultural production and wage income and the increase inmedical expenses is substantial, which reinforces our earlierargument that although the benefits of coal mining have beenshared by the entire state, the social costs associated with it aredisproportionately borne by local communities. In addition, thesocial and psychological cost of living under life-threateningpollution cannot be quantified, and by adding those costs, the totalcosts of coal mining could be much higher. The way in whichenvironmental externalities have occurred can be attributedmainly to market, policy, and institutional failures. Under thesecircumstances, the extent to which the two mainstream economicapproaches – the Pigouvian approach and the Coaseian approach,which are widely known to be effective in tackling environmentalproblems – apply needs to be tested. An attempt is made here toreflect critically on current environmental problems in the studyarea in light of the three above-mentioned failures.

6.1. Market failure

An unacceptable level of air and water pollution has resulted innegative externalities, which, in turn, have affected ruralcommunities. These costs are not reflected in the price of coaland electricity generated using the coal mined from the region.That is, markets fail to internalize externalities. Moreover, a pricingmechanism for mine pollution between victims and polluters isabsent. This could work effectively, provided that property rightsare well defined and enforced. Nevertheless, in the present case, airand water bodies, which are polluted, can be considered an open-access and common property resource (CPR). Both local people andmining industries are using these resources but because no partyowns them, negotiations between the victims and polluters havenot and cannot take place. This is what the Coaseian approachemphasizes—specifically, internalizing the externality at zerotransaction cost. In reality, there are many victims. Putting themin a position to negotiate would involve some costs, violating thezero-transaction-cost assumption of the Coaseian approach.

6.2. Policy failure

When a market mechanism fails to address certain environ-mental externalities, third-party intervention could help tominimize externalities. This is known as an interventionist orPigouvian approach, which advocates that the state intervene withvarious effective policy instruments. The Pigouvian approach tonegative externalities would need a robust regulatory system tocontrol and regulate the pollution. In Odisha, the state PollutionControl Board (PCB) has acquired more powers since theenactment of the Environmental Protection Act, 1986. Accordingto this Act, the PCB can order the closure of mines. Moreover, it isobligatory on the part of mining companies to obtain a license fromthe PCB. Although these sound effective on paper, in reality,political intervention and legal bottlenecks hinder their imple-mentation. It appears that pollution problem were sidelined tinorder o promote rapid industrialization and electricity generationin the state.

The villagers interviewed complained repeatedly about the PCBand MCL, particularly with regard to the loss of crops, damage tocattle, and effects on human health. No action has been initiatedagainst the mining companies; instead, people who have voicedcomplaints publically have been transferred to another location aspunishment. Therefore, neither social nor environmental issuescan be tackled by laws alone. The law needs to be implementedfully, and assessed to determine whether or not it is capable ofaddressing environmental concerns effectively. Much of this is, ofcourse, a function of political will.

6.3. Institutional failure

In the wake of market and policy failures, an institutionalapproach can be a viable alternative method to tackle environ-mental problems. Collective action of all of the relevant agents orvictims is one such solution. Community action is needed in orderto improve bargaining position or for residents to realisticallypressure regulatory bodies to respond to problems. In our studylocations, community action, though present, was not effective.People of the affected villages organized an economic blockade inthe Ib Valley coal mine area in 2006 and 2008. Many villagers,including women, were arrested. The villagers demanded reloca-tion, jobs at the mines, adequate compensation for their land, andfor the implementation of better control measures for dustpollution. The company promised jobs and compensation but todate, very few people have benefitted. It was also found that if anyfamily members of an MCL employee participated in anymovement against the company, that employee was suspendedfrom his or her job or transferred to other places. Hence,households have no option but to face environmental degradationhead-on if they value their jobs. Villagers have therefore stoppedobjecting to mine mining pollution and are waiting patiently to berelocated.

The present case study is a classic example of failure on allfronts, which is mainly due to the nexus between industries,policymakers and politicians, coupled with diverse economicinterests among the households found in the study areas. It is clearthat passing laws and creating institutional structures is necessarybut insufficient to address the environmental problems associatedwith mining on their own.

Acknowledgments

The financial support, in the form of an institute fellowshipfrom the Indian Institute of Technology Kharagpur, is gratefullyacknowledged. The authors would like to thank the two anony-mous referees for their insightful comments and suggestions,which have helped to improve the paper immensely. Thanks arealso due to Dr. Pulak Mishra and Mr. Uma Charan Pati for theirexcellent comments on earlier versions of the paper.

References

Alberini, A., Krupnik, A., 2000. Cost-of-illness and WTP estimates of the benefits ofimproved air quality: Evidence from Taiwan. Land Economics 76 (1), 37–53.

Aragon, F.M., Rud, J.P., 2013. Modern industries, pollution and agriculturalproductivity: Evidence from Ghana. International Growth Centre WorkingPaper.

Atkinson, G., Hamilton, K., 2003. Savings, growth and the resource curse hypothesis.World Dev. 31 (11), 1793–1807.

Bateman, et al., 2003. Economic Valuation with Stated Preferences Techniques.Edward Elgar Publishing, London.

Behera, B., Reddy, V.R., 2002. Environment and accountability: impact of industrialpollution on rural communities. Econo. Political Weekly 37 (3), 257–265.

Bogahawatte, C., Herath, J., 2011. Air quality and cement production: examining theimplications of point source pollution in Sri Lanka. In: Haque, A.K.E., Murty, M.N., Shyamsundar, P. (Eds.), Environmental Valuation in South Asia. CambridgeUniversity Press, Delhi, pp. 328–347.

Centre for Science and Environment, 2008. Rich land poor people—is sustainablemining possible? Sixth Citizens Report. State of Indias Environment.

Chaulya, S.K., 2004. Assessment and management of air quality for an opencast coalmining area. J. Environ. Manag. 70 (1), 1–14.

Clougherty, J.E., Kubzansky, L.D., 2009. A framework for examining social stress andsusceptibility to air pollution in respiratory health. Environ. Health Perspect.117(9), 1351–1358.

CMRI,1998. Determination of emission factor for various opencast mining activities,Report GAP/9/EMG/MOEF/97, Central Mining Research Institute, EnvironmentalManagement Group, Dhanbad, India.

El Azab, H.E.M., et al., 2015. The impact of informal irrigation practices on soildrainage condition, soil pollution and land suitability for agriculture in El Safarea of El Giza Governorate. Egypt. J. Remote Sens. Space Sci. doi:http://dx.doi.org/10.1016/j.ejrs.2015.04.004.

P. Hota, B. Behera / The Extractive Industries and Society 2 (2015) 683–693 693

Government of Odisha, 2014. Economic Survey of Odisha 2013–14, Directorate ofEconomics and Statistics, Bhubaneswar.

Gupta, U., 2011. Estimating welfare losses from urban air pollution using Panel Datafrom household health diaries. In: Haque, A.K.E., Murty, M.N., Shyamsundar, P.(Eds.), Environmental Valuation in South Asia. Cambridge University Press,Delhi, pp. 36–78.

Hajkowicz, S.A., Heyenga, S., Moffat, K., 2011. The relationship between mining andsocio-economic well being in Australia's regions. Resour. Policy 36, 30–38.

Hendryx, M., Ahern, M.M., 2008. Relations between health indicators andresidential proximity to coal mining in West Virginia. Am. J. Publ. Health 98 (4),669–671.

Hendryx, M., Donnel, O.H., Kimberley, H., 2008. Lung cancer mortality is elevated incoal mining areas of Appalachia. Lung Cancer . 62, 1–7.

Hilson, G.M., 2002. The future of small-scale mining: environmental and socio-economic perspectives. Futures 34, 863–872.

Holland, M., Kinghorn, S., Emberson, L., Cinderby, S., Ashmore, M., Mills, G.,Harmens, H., 2006. Development of a framework for probabilistic assessment ofthe economic losses caused by ozone damage to crops in Europe. UNECEInternational Co-operative Programme on Vegetation. CEH Project No:C02309 NEW.

Kitula, A.G.N., 2006. The environmental and socio-economic impacts of mining onlocal livelihoods in Tanzania: a case study of Geita Districts. J. Clean. Prod. 14,405–414.

Li, F., Liu, X., Zaho, D., Wang, B., Jin, J., Hu, D., 2011. Evaluating and modelingecosystem service loss of coal mining: a case study of Mentougou District ofBeijing, China. Ecol. Complex 8, 139–143.

Lim, et al., 2012. A comparative risk assessment of burden of disease and injuryattributable to 67 risk factors and risk factor clusters in 21 regions,1990–2010: asystematic analysis for the global burden of disease study 2010. The Lancet 380,2224–2260.

Lin, Y.C., Huang, S.L., Budd, W.W., 2013. Assessing the environmental impacts ofhigh-altitude agriculture in Taiwan: a Driver-Pressure-State-Impact-Response(DPSIR) framework and spatial emergy synthesis. Ecol. Indic. 32, 42–50.

Maurya, T., Karena, K., Vardhan, H., Aruna, M., Raj, M.G., 2015. Effects of heat onunderground mine workers. Procedia Earth Planet. Sci. 11, 491–498.

Merten, G.H., Minella, J.P.G., 2013. The expansion of Brazilian agriculture: soilerosion scenarios. Int. Soil Water Conserv. Res. 1 (3), 37–48.

Mishra, P.P., Pujari, A.K., 2008. Impact of mining on agricultural productivity: a casestudy of the Indian state of Odisha. South Asian Econ. J. 9 (2), 337–350.

Mishra, P.P., Reddy G., 2009. Mining in forest area- problems, causes and concerns: areview. RULNR-CESS Working Paper Series No. 1, July, 2009.

Mishra, P.P., 2009. Coal Mining and rural livelihoods: case of the Ib Valley coalfield,Orissa. Econ. Political Weekly 44, 117–123.

Mishra, P.P., 2010. Economic valuation of health impacts in a coal mining region. Rev.Dev. Change 14 (2), 183–200.

Mohapatra, H., Goswami, S., Dey, D., 2010. Coal mine dust concentration and rate oftuberculosis infection around Ib Valley coal field, Orissa, India. J. Environ. Biol.31 (6), 953–956.

MSHA (2007). US Department of Labor Mine Safety and Health Administration.http://www.msha.gov (accessed 20.02.14.).

OSPCB, 2010. Action plan for abatement of pollution in critically polluted industrialclusters (Ib-Valley Jharsuguda Area). State Pollution Control Board Orissa,Bhubaneshwar.

Ostro, B., 1994. Estimating the health effects of air pollutants: A method with anapplication to Jakarta. Policy Research Working Paper 1301. The World Bank,Washington.

Pearce, David, 1996. Economic valuation and health damage from air pollution inthe developing world. Energy Policy 24 (7), 627–630.

Reddy, V.R., Behera, B., 2006. Impact of water pollution on rural communities: aneconomic analysis. Ecol. Econ 58, 520–537.

Sach, L., Warner, A., 2001. Natural resources and economic development: the curseof natural resources. Eur. Econ. Rev. 45, 827–838.

Saha, S., Pattanayak, S.K., Sills, E.O., Singha, A.K., 2011. Under-mining health:environmental justice and mining in India. Health Place 17, 140–148.

Schatlez, S.J., Stewart, B.W., 2012. A provenance study of mineral matter in coal fromAppalachian Basin coal mining regions and implications regarding therespirable health of underground coal workers: A geochemical and Nd isotopeinvestigation. Int. J. Coal Geo. 94, 123–136.

Shandro, J.A., Veiga, M.M., Shoveller, J., Scoble, M., Koehooorn, 2011. Perspectives oncommunity health issues and the mining boom-bust cycle. Resour. Policy 36,178–186.

Sinha, S., Bhattacharya, R.N., Banerjee, R., 2007. Surface iron ore mining in easternIndia and local level sustainability. Resour. Policy 32, 57–68.

Skinner, J.A., Lewis, K.A., Bardon, K.S., Tucker, P., Catt, J.A., Chambers, B.J., 1997. Anoverview of the environmental impact of agriculture in the U.K. J. Environ.Manag. 50, 111–128.

Spash, C.L., 1997. Assessing the economic benefits to agriculture from air pollutioncontrol. J. Econ. Surv. 11 (1), 47–70.

Stephens, C., Ahern, M., 2001. Worker and community health impacts related tomining operations: a rapid review of the literature. http://pubs.iied.org/pdfs/G01051.pdf (accessed 14.06.15.).

Vincent, J.R., 2011. Valuing the environment as a production input. In: Haque, A.K.E.,Murty, M.N., Shyamsundar, P. (Eds.), Environmental Valuation in South Asia.Cambridge University Press, Delhi, pp. 36–78.

Wei, J., Guo, X., Marinova, D., Fan, J., 2014. Industrial SO2 pollution and agriculturallosses in China: evidence from heavy air polluters. J. Clean. Prod. 64, 404–413.

WHO, 2009. Basic Documents, 47th ed. World Health Organization, Geneva,Switzerland http://apps.who.int/gb/bd/PDF/bd47/EN/basic-documents-47-en.pdf (assessed 30.03.15.).

WHO, 2014. Burden of Disease from Household Air Pollution For 2012. World HealthOrganization, Geneva, Switzerland http://www.who.int/phe/health_topics/outdoorair/databases/FINAL_HAP_AAP_BoD_24March2014.pdf (assessed on26.03.2014)..

World Bank, 2007. Cost of pollution in China – economic estimates of physicaldamages. http://siteresources.worldbank.org/INTEAPREGTOPENVIRONMENT/Resources/China_Cost_of_Pollution.pdf (accessed 15.03.14.).

World Bank, 2013. Economic growth and environmental sustainability: what are thetrade-off? Diagnostic Assessment of Select Environmental Challenges, 2(70004-IN). Disaster Management and Climate Change Unit, SustainableDevelopment Department, South Asia Region.

Zhang, D., Anunan, K., Seip, H.M., Larssen, S., Liu, J., Zhang, D., 2010. The assessmentof health damage caused by air pollution and its implication for policy makingin Taiyuan, Shanxi, China. Energy Policy 38, 491–502.

本文献由“学霸图书馆-文献云下载”收集自网络，仅供学习交流使用。

学霸图书馆（www.xuebalib.com）是一个“整合众多图书馆数据库资源，

提供一站式文献检索和下载服务”的24 小时在线不限IP

图书馆。

图书馆致力于便利、促进学习与科研，提供最强文献下载服务。

图书馆导航：

图书馆首页 文献云下载 图书馆入口 外文数据库大全 疑难文献辅助工具