| Literature DB >> 35457622 |
Muhammad Haseeb Raza1, Muhammad Abid2, Muhammad Faisal3, Tingwu Yan4,5, Shoaib Akhtar6, K M Mehedi Adnan7.
Abstract
The burning of crop residue in the open field has become a significant concern for climate change mitigation efforts worldwide. This practice has led to air quality impairment, smog, haze, heat waves, and different health problems. These could be avoided by adopting sustainable crop residue management practices (SCRMPs) and enabling farmers to engage in SCRMPs. Assessing the health effects at the household level is critical for understanding this problem and finding a solution. Using the primary dataset of 420 farmers from Punjab, Pakistan, we estimated the incurred impacts and costs of crop residue burning. We calculated the health and environmental benefits associated with adopting SCRMPs by comparing the two groups of farmers (adopters and non-adopters). Furthermore, we used a propensity score matching technique to measure the causal impact of SCRMPs adoption on health costs. The findings showed that a surprisingly large number of farmers are all aware of the adverse effects of residue burning, and many do not burn crop residues and instead use SCRMPs. This study found that households with chronic and non-chronic diseases become acute, and the severity increases during the burning period. They spend USD 13.37 to USD 8.79 on chronic and non-chronic diseases during the burning season, respectively. Consequently, the use of SCRMPs has a positive effect on healthcare costs. Our study findings highlight the meaningful implications for developing a new policy to promote the sustainable utilization of crop residues and enhance their adoption in Pakistan.Entities:
Keywords: Pakistan; environmental benefits; health cost; propensity score matching; sustainable crop residue management
Mesh:
Year: 2022 PMID: 35457622 PMCID: PMC9032433 DOI: 10.3390/ijerph19084753
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 4.614
Figure 1Research area.
Figure 2Sampling strategy.
Figure 3Conceptual framework.
Descriptive statistics.
| Variables | Description | Percentage (%) | |
|---|---|---|---|
| Age | Up to 30 years | 21.9 | |
| 31–50 | 47.9 | ||
| 51–70 | 28.8 | ||
| 71 and above | 1.4 | ||
| Education level | Up to the Primary education | 44.3 | |
| Elementary School | 20.5 | ||
| High School | 23.6 | ||
| College or above | 11.7 | ||
| Farm size | Up to 4 acres | 38.3 | |
| 5–12 acres | 32 | ||
| 13–24 acres | 15.2 | ||
| More than 24 acres | 14.5 | ||
| Annual income | Up to PKR 250,000 | 22.9 | |
| PKR 250,000–500,000 | 33.8 | ||
| PKR 500,000 and above | 43.3 | ||
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| Adoption | Dummy takes the value 1 if do not burn and 0, otherwise | 50.8 | 48.9 |
| Tube well ownership | Dummy takes the value 1 if own tube well and 0, otherwise | 44 | 66 |
| Mobile | Dummy takes the value 1 if own mobile and 0, otherwise | 97 | 3 |
| Tractor Trolley | Dummy takes the value 1 if own Tractor trolley and 0, otherwise | 24.5 | 75.5 |
| Rotavator | Dummy takes the value 1 if own rotavator and 0, otherwise | 29.5 | 70.5 |
| Extension | Dummy takes the value 1 if use extension service and 0, otherwise | 62.5 | 37.5 |
| Credit | Dummy takes the value 1 if use credit and 0, otherwise | 67.5 | 32.5 |
| Thresher | Dummy takes the value 1 if have Thresher and 0, otherwise | 12.6 | 87.4 |
| Weather forecast | Dummy takes the value 1 if check weather forecast and 0, otherwise | 63.33 | 36.66 |
| Canal water information | Dummy takes the value 1 if collect canal water information and 0, otherwise | 46.6 | 53.4 |
| Member of Farmers organization | Dummy takes the value 1 if member of FO and 0, otherwise | 5 | 95 |
| Disc Plough | Dummy takes the value 1 if own Disc plough and 0, otherwise | 42.6 | 57.3 |
| Gujranwala | Dummy takes the value 1 if from Gujranwala and 0, otherwise | 33 | 67 |
| Faisalabad | Dummy takes the value 1 if from Faisalabad and 0, otherwise | 33 | 67 |
| Rahim Yar Khan | Dummy takes the value 1 if from Faisalabad and 0, otherwise | 33 | 67 |
Health damages due to crop residue burning (percentage response of respondents).
| Health Impacts | Gujranwala | Faisalabad | R. Y. Khan | Overall |
|---|---|---|---|---|
| Coughing | 49.6 | 40.3 | 45 | 45 |
| Eye irritation | 42 | 33 | 24.5 | 33 |
| Headache | 32 | 28 | 32.9 | 31 |
| Nausea | 35 | 21 | 32 | 29 |
| Skin irritation | 27.6 | 19 | 23.3 | 23 |
| Respiratory Allergies | 37.6 | 12 | 17.1 | 22 |
| Blurred vision | 20 | 12.3 | 18.5 | 17 |
| Dizziness | 11 | 7.1 | 15 | 11 |
| Asthma | 11 | 13.9 | 5.2 | 10 |
| Bronchial Infection | 14.1 | 10.1 | 6 | 10 |
Some respondents reported more than one health impact, so the percentage would not be equal to 100.
Relating the intensity of crop residue burning with the intensity of suffering (percentage response of respondents’ perceptions).
| Districts | Do You Think the Intensity of Crop Residue Burning Increase (Yes) | Do You Think Health Impact Increase Due to Crop Residue Burning (Yes) |
|---|---|---|
| Gujranwala | 56% | 69% |
| Faisalabad | 41.6% | 55% |
| Rahim Yar Khan | 54.3% | 62% |
Other issues associated with crop residue burning. (percentage).
| Districts | Loss of Work Productivity for Working Members | Having Observed Accident Happening Due to Smoke | No. of Injuries/Deaths Due to Smoke |
|---|---|---|---|
| Gujranwala | 31 | 23 | 27 |
| Faisalabad | 23 | 14 | 13 |
| Rahim Yar Khan | 35 | 34 | 22 |
Health costs incurred caused by crop residue burning (USD/season).
| District Name | Traveling/ | Self-Treatment | Preventive Measure | Medication (Doctor, Hospital Charges/Medicine Cost) | |
|---|---|---|---|---|---|
| Chronic Diseases | Non-Chronic Diseases | ||||
| Gujranwala | 1.85 | 0.73 | 0.85 | 13.21 | 9.63 |
| Faisalabad | 2.13 | 1.52 | 0.75 | 12.87 | 8.32 |
| RYK | 2.27 | 1.32 | 0.67 | 14.02 | 8.43 |
| Total | 2.08 | 1.20 | 0.76 | 13.37 | 8.79 |
Figure 4Farmers’ residue management practices in three Punjab districts.
Figure 5Constraints faced by the farmers.
Figure 6Relationship between adaptation and socio-demographic properties.
Knowledge about the hazardous impact of crop residue burning (percentage).
| Particulars | Adopters | Non-Adopters |
|---|---|---|
| Are you aware of the harmful impact of crop residue burning on health? | ||
| Yes | 55.07 | 75.58 |
| No | 44.93 | 24.42 |
| Are you aware of different alternative crop residue management practices? | ||
| Yes | 59.42 | 58.68 |
| No | 40.57 | 41.31 |
Estimation of the propensity score matching through logistic regression.
| Variables | Estimator | Standard Deviation | Z-Value |
|---|---|---|---|
| Education | 0.17 | 0.031 | 5.56 *** |
| Age | −0.01 | 0.009 | −1.12 |
| Income | 5.89 | 0.001 | 0.11 |
| Farm size | 0.04 | 0.01 | 2.44 ** |
| Tube well ownership | 0.39 | 0.15 | 2.63 *** |
| Tractor Trolley | 1.13 | 0.47 | 2.39 ** |
| Rotavator | −1.65 | 0.55 | −3.01 *** |
| Disc Plough | 1.84 | 0.34 | 5.43 *** |
| Thresher | 0.24 | 0.58 | 0.43 |
| Distance output market | 0.01 | 0.01 | 0.89 |
| Paved Road | −0.07 | 0.05 | −1.27 |
| Member of Farmers organization | 0.991 | 0.64 | 1.54 |
| Extension Services | 0.65 | 0.32 | 1.98 ** |
| Weather forecast | −1.45 | 0.35 | −4.14 *** |
| Canal water information | 0.55 | 0.30 | 1.83 * |
| Credit | 0.10 | 0.28 | 0.36 |
| Mobile | 0.80 | 1.01 | 0.79 |
| Gujranwala | −1.21 | 0.34 | −3.51 *** |
| Faisalabad | −0.56 | 0.31 | −1.79 * |
| Rahim Yar Khan | Omitted | ||
| Number of observations (420), LR chi2 (19) (174.30), Prob > chi2 (0.0000), Log likelihood (−203.89), Pseudo R2 (0.2994) | |||
*** p < 0.01, ** p < 0.05, * p < 0.1.
Impact of adoption on health costs.
| Outcome | ATT | ATE | No. of Treated | No. of Control |
|---|---|---|---|---|
| Health Cost | −312 ** | −897 ** | 107 | 107 |
** 5% level of significance.
Impact of balancing covariates before and after matching.
| Indicators of Covariates Balancing | Before Matching | After Matching |
|---|---|---|
| Pseudo R2 | 0.2994 | 0.0721 |
| 0.001 | 0.23 | |
| F-stat | 79.22 | 10.44 |
| Mean standardized difference | 0.26 | 0.03 |
| Total% bias reduction (%) | - | 56 |