| Literature DB >> 35897444 |
Amare Tesfaw1, Feyera Senbeta2, Dawit Alemu3, Ermias Teferi2.
Abstract
Today, evaluating ecological wellbeing and ecosystem services is becoming a great concern towards conserving the natural resource base. Healthy functioning ecosystems have fundamental roles for aiding humankind to lead a healthy life and ensure an improved social welfare. Estimating the non-market benefits of ecosystem services can help experts and the public frame policy directions designed for landscape development. The ecosystem of the Eucalyptus hotspot highlands of northwestern Ethiopia, where this study was carried out, provides services that are essential to changes in the life of the society and biodiversity. However, in recent years, the ecosystem is facing a serious threat from intensive monoculture plantations of Eucalyptus. This has resulted in transformation of the cultural landscapes and a loss of biodiversity. The problem in turn calls for designing appropriate ecological improvement programs. Thus, the current study examined the preferences of residents concerning this area and estimated their willingness to pay (WTP) for the proposed ecosystem improvement programs using a Choice Experiment approach. Data were aggregated from 388 residents using a questionnaire survey in January 2020. The survey contained ecological improvement schemes and a hypothetical event by which respondents expressed their willingness to pay a yearly utility fee as a compensation for the improvement programs. Results showed significant differences in resident preferences towards the proposed ecological improvement attributes. The findings also indicated that the socioeconomic backgrounds of residents contributed for the heterogeneity in their WTP for ecological improvement schemes. Accordingly, the marginal willingness to pay of residents was USD 205/person/year for the respective ecological improvement attributes. The findings suggest that policy makers should consider such attribute-based public preferences while planning landscape development and conservation programs. This study can provide vital policy implications and contribute to knowledge as it presents how the non-market valuations of ecosystems help maximize social welfare.Entities:
Keywords: Eucalyptus; choice experiment; marginal willingness to pay; monoculture plantations; non-market benefit
Mesh:
Year: 2022 PMID: 35897444 PMCID: PMC9332550 DOI: 10.3390/ijerph19159073
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 4.614
Figure 1Map of the study area.
Conceptual attributes and levels.
| Attribute | Description/Assumptions | Levels |
|---|---|---|
| Intended land use plan (scale of | Plots devoid of | 25 |
| 50 | ||
| 75 | ||
| Fertility of land 1 | Plantations focusing mainly on marginal lands will have reduced impact ecosystem | Not fertile |
| Fertile | ||
| Highly fertile | ||
| Number of other tree species to be grown | The more the number of other tree species, the better will be the ecological wellbeing | 2 |
| 3 | ||
| 4 | ||
| Payment for change in attribute (USD) | How much are households willing to pay as a compensation for restriction of | 31.21 |
| 62.41 | ||
| 93.62 |
The ‘numeraire’ used throughout this paper are in USD using the 21 January 2020 exchange rate ($1 USD = ETB 32.044. 1 Land fertility is a relative term, which is based on farmers’ ratings. Marginal lands that are not used for the cultivation of food crops, except avena and lupin, are categorized as “not fertile”. Lands that support the cultivation of wheat, barley, flax, etc. are categorized as “fertile”. “Highly fertile” lands exceptionally permit the cultivation of maize, potato, bean and teff.
Figure 2Sample profile of choice sets.
Summary of variables used in the choice model.
| Variable | Description |
|---|---|
| Attribute variables | |
| Intended land use plan (scale of | Plots devoid of |
| Fertility of land planned for | Plantations of |
| Number of other tree species to be grown | The more the number of other tree species, the better will be the ecological wellbeing |
| Payment (Birr) | Charge incurred as a compensation for restriction of |
| Non-attribute variable | |
| Income source | Sources of income from crop, livestock, trading, rentals, etc. |
| Age | Age of household head |
| Sex | Sex of the household head |
| Family Labor | Number of family labor available (continuous) |
| Total land size (ha) | Size of total land holdings of households (ha) |
| Slope (%) | Slope of cultivable plots (%) |
| Number of oxen | Number of oxen owned by a household |
Sociodemographic characteristics of respondents.
| Characteristics | Variable | Value |
|---|---|---|
| Demography | Average family size | 6 |
| Dependency ratio (%) | 64 | |
| Age | 48 | |
| Gender (%) | ||
| Female | 10 | |
| Male | 90 | |
| Marital status (%) | ||
| Married | 80.9 | |
| Not Married | 5.2 | |
| Divorced | 7.7 | |
| Widowed | 6.2 | |
| Literacy status (%) | Cannot read and write | 8.93 |
| Read and write | 80.51 | |
| Religious school | 4.12 | |
| Grade 1–6 | 5.15 | |
| Grade 7–12 | 1.29 | |
| Average land size (ha) | 1.37 | |
| Farm resource and income | Average annual income | |
| Crop | 502.98 | |
|
| 881.20 | |
| Livestock | 330.07 | |
| Non-farm sources | 211.43 |
Result of the multinomial logit model.
| Attribute Variables | Model 1 | Model 2 | Model 3 |
|---|---|---|---|
| Coefficient | Coefficient | Coefficient | |
| Land use plan | 0.42199 (0.05109) *** | 0. 42128 (0.05139) *** | 0.41006 (0.05142) *** |
| Fertility | 0.23473 (0.05459) *** | 0.23437 (0.05493) *** | 0.22117 (0.06120) ** |
| Other trees | 0.48953 (0.05362) *** | 0.49173 (0.05393) *** | 0.50125 (0.05421) *** |
| Payment | 0.00031 (0.00003) *** | 0.00031 (0.00003) *** | 0.00033 (0.00004) ** |
| Constant | −3.32501 (0.19608) *** | ||
| Income | 0.10495 (0.04776) *** | 0.12136 (0.04991) *** | |
| Age | 0.01499 (0.00498) *** | 0.01501 (0.00611) | |
| Sex | −0.15990 (0.13622) | −0.16030 (0.14501) | |
| Family labor | 0.16043 (0.03937) *** | 0.16171 (0.04015) *** | |
| Land size (ha) | −0.00366 (0.06307) | −0.00386 (0.08821) | |
| Slope (%) | 0.11086 (0.08254) | 0.11104 (0.08715) | |
| Number of oxen | −0.11045 (0.05235) | −0.11630 (0.06104) | |
| Constant | −4.90173 (0.41072) *** | ||
| Income *Land use plan | 0.31090 (0.03281) ** | ||
| Income *Fertility | 0.03931 (0.01410) | ||
| Income *Other trees | 0.21095 (0.09142) *** | ||
| Age *Land use plan | 0.02657 (0.03120) *** | ||
| Age *Land fertility | −0.03531 (0.02381) ** | ||
| Age *Other trees | 0.31140 (0.00201) | ||
| Family labor *Land use | 0.07142 (0.03224) *** | ||
| Family labor *Fertility | 0.21705 (0.03496) ** | ||
| Family labor *Other trees | 0.362510 (0.04184) | ||
| Constant | −6.03272 (0.49035) *** | ||
| Log likelihood | −3406.2996 | −3382.7425 | −3391.5241 |
| Pseudo R2 | 0.0458 | 0.0528 | 0.0762 |
Number of obs = 3492; Prob > Chi2 = 0.0000; Parenthesized figures are standard deviations; ** and *** indicate level of significance at 5% and 1% probability. * in between two variables indicate interaction.
MWTP for a change in attributes.
| Change in Attribute | MWTP (USD/Person/Year) |
|---|---|
| Intended land use plan (scale of | 54.83 (26.75%) |
| Shift of plantation from fertile to non-fertile plots | 24.35 (11.88%) |
| Increasing plantation of other tree species from minimal to more | 125.82 (61.38%) |
| Total | 205.00 (100%) |
Note: MWTP values are converted to USD after running the multinomial logit model.