How Do Energy Demand and Socioeconomic Factors Drive Localities Towards Deforestation and Carbon Emissions in Pakistan?

Pakistan, the 6 th most populous country in the world is also classied as the 7 th most-affected by climate change. To blame is the ever-rising population growth, leading to higher energy demand that is dragging the rural communities towards forest resource depletion in order to meet household energy needs. It is impacting an already limited 4% of the country’s forest reserves and excessive carbon emissions. This research has analyzed deforestation rate, biomass and carbon losses, and CO 2 emissions in the Malakand Division, Pakistan. The data sources and models used were Landsat (8), GIS, and Remote Sensing, coupled with self-administered 521 household questionnaires deployed in 23 villages in the proximity of forest land. The results conrmed deforestation at the rate of 0.74%yr -1 , corresponding to the total emission of 1352055.64 MgCO 2eq yr -1 over a period of 17 years (2000-17). The risk of losing forest resources stands at 8141 species yr -1 . The drivers behind deforestation found were fuelwood collection up-to 8501720 kg to meet the energy needs of households and commercial activities. The reasons behind this exhaustive deforestation and carbon emissions as per the household survey were abridged formal sector, low off-farm wages, and non-availability of credits to shift to other sources of income which has also affected the affordability of the social services. The study calls for the provision of alternative livelihood opportunities and effective forest management on the REDD+ mechanism to protect forest resources and address global warming.


Introduction
Forests are the critical elements in maintaining the carbon cycle. They are the largest source of carbon sink/sequestration in the open environment that preserves air quality (Coulston et al., 2015;Espirito-Santo et al., 2014). On the contrary, the global problem of deforestation and forest degradation is predominantly faced with Land Use Land Cover Changes (LULCC), thus accounting for 17-25% of the Greenhouse Gasses (GHGs) (Le Quéré et al., 2015;Bernstein et al., 2008). The deforestation resultant carbon emissions up to 1.2 Pg C yr − 1 have been threatening (regional and global) motives of land and forest management (Joshua et al., 2017;Van der Werf et al., 2009).
Globally, the forest land has been converted into other land categories (e.g., infrastructure land) as foremost human activity has uctuated the stock of forest carbon (Zhou et al., 2013;Pan et al., 2011). This uctuation has led the world to excessive forest land conversion, owing to deforestation and emission of 156 Pg C into the open atmosphere during 1850-2005 time period (Houghton, 2003). Changes in land use, therefore, are the signi cant contributors to climate change and global warming on transmitting CO 2 and other GHGs (Baccini et al., 2012a). This complex and increasing issue gives rise to a key problem for the research community in the eld (Thompson et al., 2011).
In the debate of LULCC, industrialization, commercialization, land exploitive agricultural practices, etc. have been drastically changing the natural atmosphere due to their greater share of CO 2 emissions, besides issue like soil erosion, ooding, biodiversity loss, and disturbing the water cycle (Espindola et al., 2011;Keenan et al., 2015). More recently (2016-2017) the world is faced with CO 2 emissions equal to the average CO 2 emissions over the last complete decade (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) (WMO, 2018). Furthermore, CO 2 concentration is forecasted to double by 2030, followed by a subsequent 1.5-4.5°C rise in Global Mean Temperature (GMT) (Balogh, 2020). The rise in GMT due to deforestation (over 13 million hectors/year) is further linked to biodiversity loss and disturbance in water regulations (Baccini et al., 2012a). Reversing or reducing deforestation is a very complex process due to the fundamental factors; agricultural expansion, development of infrastructure, timber extraction, and additional set of complex institutional, governmental and speci c regional factors (Stren, et al., 2006;Eliasch, 2008;Olsen and Bishop 2009).
Whether natural and/or anthropogenic, the issue of deforestation and forest degradation is common to both South and Southeast Asia (in the Asian continent) that includes both (legal and illegal) unsustainable selective logging, agricultural expansion, subsistence agriculture, overgrazing, encroachments, extraction of forest wood for fuel, charcoal and construction projects (Gayen and Saha, 2018;Bahugunaa et al., 2016;Stibig et al., 2014;Hosonuma et al., 2012;Kissinger et al., 2012;Reddy et al., 2009;Lele and Joshi, 2009). Deforestation and its irreplaceable consequences (e.g., biodiversity loss, soil erosion, disruption in water cycle) are the familiar sources of unemployment and rural poverty (Meyfroidt et al., 2013;Kissinger et al., 2012;Espindola et al., 2011). Here, Hansen et al., (2012) revealed that fuelwood energy and subsistence agriculture are the leading cause of deforestation and forest degradation in the South-Asia. The intensity of the issue in region is linked to socio-economics of the rural societies as deforestation contributes to the local community's income generation, energy use, and cleared land for subsistence agriculture. As a signi cant source of income and energy in the rural settings including South-Asia, around 1.2 billion people are dependent on agro-forestry, while over 60 million people generate their income and energy from the forest resources (Tufail et al., 2020;Insaidoo et al., 2012;Gorte and Sheikh, 2010;World Bank, 2003). This fast phase deforestation and forest degradation phenomena in the developing countries are contributing towards 18-20% of the total global GHG emissions leading to global warming (Insaidoo et al, 2012;Owusu et al, 2011).
In South-Asian region, the depletion of forest reserves remains a well-established trend in Pakistan (Tufail et al., 2021;FAO, 2007FAO, , 2015Tariq et al., 2014;Ahmad et al., 2012;Nazir and Olabisi 2012;Ahmad and Abbasi 2011). Pakistan is experiencing a declining trend in forest cover, up to 28,000 ha annually (FAO, 2009). The situation is even more drastic in the forest-rich areas of Pakistan (e.g., Chitral) (Qamer et al., 2015;Shehzad et al., 2014). The reasons behind deforestation and forest degradation in Pakistan are linked mainly to socio-economic factors like conversion of land for agriculture, population growth construction for dwellings, commercial activities, poverty, lack of participatory governance mechanisms for forest conservation, etc. and despite the ban imposed by the provincial government of Khyber Pakhtunkhwa (KP), the situation continues to exacerbate (Haq et al, 2018;Nazir, 2009;Suleri, 2002). It remains pertinent that in the face of measures (KP green growth project, billion tree tsunami) taken by the federal/provincial public forest degradation has been hampering the country's natural capital.

Problem Statement
At the provincial level in Pakistan, over 40% of forests are located in junks in the mountainous/northern areas of KP province. In the northern areas, Malakand Division has dense forest resources. The distribution of forest covers across districts in the Malakand division) are 58% in Upper Dir, 52% in Swat, 48% in Shangla, 26% in Lower Dir, 25% in Buner, and 14% in Malakand district (GoP, 2017). The status of forest in Malakand Division by the legal de nition is 7.6% as reserved, 29.7% as communal forest, and 62.7% as protected forests. The reserved forests are in the jurisdiction of the state and communal forests are retained by the local communities or individual families. Protected forests are claimed by both the government and local communities and they are in the transitional phase of land settlement. The protected forests belong to rulers of the princely states of Chitral, Dir, and Swat that were then transferred as the State property. These protected forests have been severely deforested by the local communities due to the legally accepted rights for construction, energy, and grazing purpose.
Owing to the growing demand for timber and timber products, the price of such forest products has increased sharply in the recent past. It has led to the illegal extraction of this non-renewable forest resource. Timber ma a (gangs, partly in cooperation with larger communal owners, contractors, and trader) was involved in the illegal extraction of forest resources.
Hence, the illegal activities had started shrinking the forest area by 16.4% in Lower andUpper Dir, 31.8% in District Buner, and14.9% in Swat andShangla by 2010 (INRMP, 2010). Several studies conducted on individual districts (Qamar et al., 2016(Qamar et al., , 2012Amir et al., 2015;Rahman et al., 2014;Tariq et al, 2014;Qasim et al., 2013Qasim et al., , 2011 reported a decrease of forest area between 1997-2007, mainly due to agricultural expansion, increase in buildup area, agricultural activities, fuelwood consumption, etc. There exists a contrast in the ndings of these independent studies and government statistics where forest area shows increase because of afforestation, agroforestry, and controlling the illegal logging (UNEP & ICIMOD, 1998, pp 29-31).
Conifer forest resources in the region have been severely threatened due to fuelwood and timber collection for both subsistence and commercial purposes, besides its conversion into agricultural elds. The dependence on forest resources also remains high due to low (avg.) agricultural productivity (equal to 1382kg/ha of wheat, compared to 2264 kg/ha in KP and 2797 kg/ha in Pakistan) (GoKP, 2015). Also, the o cial forest reference levels and forest reference emissions levels are not yet available, despite recent technological advances (Lopes et al. 2019).
The local inhabitants mainly rely on forests to complement the HH energy needs, construction materials, commercial needs, subsistence, and cash needs. The problem is intensi ed by population growth (4.1% during 1998-2017 and higher population density of 409.59 persons/km 2 compared to national avg. of 235.76 persons/km 2 ) (GoP, 2017). Most of the rural population lacks the necessities of daily life in these districts. The main sources of earning are the subsistence agriculture, livestock grazing, fuel, and wood collection, that are posing an additional pressure on forests for an increased demand of fuelwood (a source of HH energy), foods (additional pressure on agricultural land and productivity), and construction (increased demand for houses and market expansion). In this context, HHs in the periphery of forests are the major causes of deforestation and forest degradation due to conversion of forest areas into farmlands, illegal encroachments, logging, and extensive extraction of forest products for local and commercial purposes.
The current study aimed to evaluate the level of deforestation and resultant CO 2 emissions, coupled extrapolating reasons behind the observed losses in the selected districts of Malakand Division, KP province. The speci c questions this study tried to answer are: 1. What was the deforestation rate for 2000, 2010, and 2017 in the study regions? 2. What were the level of fuelwood collection and timber harvesting?
3. What were the socio-economic factors driving the locality towards deforestation and forest degradation?

Study Area and Resources
This research study was conducted in the Malakand Division, Khyber-Pakhtunkhwa province, located at 34.15 0 to 35.90 0 latitude and 71.50 0 to 73.00 0 longitude (FAO, 2016). Malakand Division has forests on an extended area that range from high altitude conifer forest occupying the heights ranging from 1550m to 3300m in the western Himalaya to Hindu Kush Mountains. The area is also rich with Oaks which cover the temperate zone (at an altitude of 1500 m to 3000m) and alpine scrub and sub-alpine forests (at an altitude of 3600m to 4900m). The area has diverse ecosystems with forests, alpine pastures, shrubs, bushes, rangeland, and agriculture land at high mountainous altitudes. The temperature is low in winter (up to -10 o C). This study was conducted in six districts of Malakand Division, namely; Buner, Lower Dir, Upper Dir, Malakand, and Swat (Fig. 1). The population of Malakand division residing in an area of 29,871 km 2 is 7.5 million (GoP, 2017). In-total 23 villages were selected within these six districts for data collection (Table 1). Forest ecoregion (WWF reference maps). The most common stands are composed of r, pure or mixed with oaks, or assemblages of r and birch, pine and spruce, or cypress and cedar (Qamer et al., 2016). Also, being a crucial ecosystem for livelihoods, these forests have high conservation value (e.g., habitat for protected migratory birds).

Data Sources and Techniques
Several data sources have been used to compile a geographic database with the biophysical attributes of the area. For land cover, maps of 2000 and 2010 extracted from the maps database of the International Center for Integrated Mountain Development (ICIMOD) have been used. Maps for 2017 were constructed using Landsat-8 (L8) imagery. Three Landsat scenes that cover the study area are listed in Table 2. The mosaic was produced in Google Earth Engine API (GEE) and all bands have 30 m resolution. The Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM) with 30 m resolution was used to improve the classi cation results. From this dataset, elevation, slope, and aspect were obtained and then added as bands to the 2017 mosaic. The values of elevation, slope, and aspect were normalized to the same range as the L8 bands and then added to the mosaic. The bands included in the nal mosaic are listed in Table 3. and then used to register the images of 2010 and 2017, maintaining the RMS (Root Mean Square) error of each registration below 0.5 pixels (< 15m). The images were corrected for the sensor, atmospheric, and illumination variance sources by radiometric calibration, as suggested in the domain (Green and Hanson, 2002). The Maps of ICIMOD for the years 2000 and 2010 were reclassi ed initially from 12 classes to 6 classes (All the six forest types were combined in one class, agriculture and agriculture fellow land were combined in one class, and further classes were kept unchanged) and then to four main classes ( Here. water bodies, snow, and other land classes were kept as zero (0)  The total forest area was calculated for each district from the land use land cover change analysis. For an estimation of carbon stock, the biomass is converted into carbon, using the global carbon measuring factor (0.47) (Pandey et al. 2016;Zhang et al. 2013;Haripriva, 2000;IPCC, 2006a). While nationally adopted carbon measuring factor (0.5) has been used Ahmad et al. 2014;Nizami, 2012). Deforestation in the study area resulted in the conversion of forest land into other lands uses, such as forest land to agriculture land, rangeland, barren land, and settlement. So, the net reduction in carbon stock was calculated by 'Mg C yr − 1 ' -annual carbon loss from deforestation, and total forest carbon as Mg C ha − 1 (before deforestation and after deforestation, i.e., total forest area converted to other lands in hectare (Philip, 1994

Socio-Economic Survey & Data Analysis
The literature suggests the existence of several direct and indirect socio-economic drivers of deforestation. These drivers are classi ed as; agricultural expansion (subsistence and commercial agriculture), wood extraction (fuelwood and construction timber), market expansion (households' construction and commercial expansion), and demographic, economic, political, and institutional factors (Tufail et al., 2021;Irfan et al., 2017;Qamer et al., 2016;Qamer et al, 2012;Ali et al., 2006;Ali and Benjamin 2004;Gautam et al., 2003). The same drivers as variables were taken to study the socio-economic causes of deforestation and forest degradation in the study region (Table 4). For this reason, a eld survey was conducted at households (HH; A household is de ned as "the people sharing the same house boundary wall and using the same kitchen or cooking and sharing meals) in the peripheries/villages (i.e., within 6 km as communal rights on the forest which changes even within four to ve-kilometer range) of the natural forests in the study area. Due to the remoteness, diversity and inaccessibility of most parts/villages/far ung areas, it was impossible to include every village in the survey. Thus, sampling of accessable villages helped to re ect the most representative combinations of geographical and socioeconomic situations in the region.
It was to establish the relative value ( It's the value that is directly extracted from the forests (by local communities) for their livelihood, compared to other income sources) of the goods and services in the forests' vicinity. Before administring the nal re ned questionnaire (May-June 2017 and August-September 2017,), which was easy to understand and attempt, a pilot survey was implemented (in March 2017) amongst randomly selected households in 3 different villages in 2 Districts, i.e., Lower Dir and Malakand. In total 521 self-administered (close-ended) questionnaires were collected at HH level covering six main topics (Table 5) The data collected through the questionnaires were then organized in a spreadsheet for quality screening.   (Figures 2-4). The results of LULCC for two-period P1 (2000-10) and P2 (2010-17) showed conversion of the vast area into grassland and other lands (e.g., settlements and barren land) ( Table 6). It has been observed that Swat District (relatively) lost more forest area, followed by Upper Dir and Shangla Districts -forest area of 5110.4 Hactare (ha) in Swat, 1765.2 ha in Upper Dir, and 1300.5 ha in Shangla has already been converted into grassland. Also, the forest area converted to settlement and barren land also remained high (i.e., 1897.7 ha in Swat, followed by 763.7 ha and 583.9 ha in Upper Dir and Buner, respectively).
The land-use change analysis for the P2 also con rmed the conversion of forest area (11790.5 ha) to agriculture land in Swat -the highest in all districts. Whereas, 382.5 ha area was converted into agriculture land in Malakand -the lowest in the whole study area. Similarly, 28422.6 ha of forest land was converted to grassland in Buner, followed by 16967.3 ha in Swat -both the highest gures. Also, 9786.2 ha of forest land was converted to grassland in Malakand. Conversion of forest land to other lands in P2 has increased in the two districts, i.e., Malakand and Shangla, while in all other districts namely, Buner, Lower Dir, Swat, and Upper Dir experienced a decreased trend (Table 7). It is, therefore, con rmed that the whole study area suffered from signi cant land-use changes, hence the conversion of forest land to other land categories between P1 (2000-10) and P2 (2010-17) see Figures 5 and 6. The average deforestation rate in the study area remained 0.63% in P1 and 0.84% in P2. A high deforestation rate was seen in Lower Dir, followed by Shangla and Upper Dir. Opposite to this the lowest rate of deforestation was in the district Malakand in P1. It has been observed that the deforestation rate slightly decreased in Lower Dir, Upper Dir, and Buner in the second period but increased in Malakand and Shangla region in P2 (Table 8). It is established that the study region has lost a total of 108921.16 ha at the rate of 0.63% annually in P1 and 98223.63 ha at the rate of 0.84% per annum in P2 (Table 8).
The description of Figure 7 showed the comparison of the deforestation rate between the P1 and P2. The gure suggested that the deforestation rate has increased at an alarming rate in District Malakand and Shangla. In contrast, it has signi cantly decreased in District Buner and slightly decreases in District Upper Dir, Swat, and Lower Dir.  (Table 10). Furthermore, biomass and carbon loss from timber harvesting and fuelwood harvesting were the main reasons behind the problem (Table 11). The values of the losses from commercial wood harvesting estimated to be 1288.10 Mg, 651.28 Mg, and 540.40 Mg in Swat, Malakand, and Buner, respectively (The value of biomass and carbon stock was calculated from household's integrated survey using factor 0.5 for biomass and 0.47 for carbon). Whereas, the same losses due to fuelwood harvesting were linked to HHs' primary energy source of the community-dwelling in the peripheries of forests. The difference in biomass and carbon loss from commercial wood harvesting in the study region was also linked to exogenous (timber ma a) factors. This extensive and uncontrolled loss of biomass (due to deforestation and forest degradation) and carbon emissions are reinforcing global phenomenon in lieu of HH fuelwood consumption (Ahmad et al., 2018). So, it remains imperative to explore the actual causes of the problem along with its severity in the region.

Causes of Deforestation
The results of the study revealed intensi ed deforestation and forest degradation problem in the whole study area, which was linked to fuelwood harvesting, with HH responsible for burning a total of 212543 bundles of fuelwood. Fuelwood used in Lower Dir is 53477 accounting for 25% of the total fuelwood which is the highest while in the remaining districts the fuelwood used followed a similar pattern. This extensive harvesting was to meet HH fuel consumption and then selling (along with Non-timber Forest Products-NTFPs) to generate income. It is coupled with un-ending forest harvesting for construction of physical infrastructure e.g., houses, shops, etc. The HH collects a total of 37031 pieces of construction timber annually, of which 31% (11710 pieces) collection occurred in district Upper Dir followed by Malakand 22% and Buner 18%. Also, issues like, illegal logging was found on the peak that has been posing incremental impact on the limited forest resources (Table 12). Furthermore, the pressure on forest resources was found linked to high population growth, forest cutting for agricultural expansion, and increased activities of residential construction.   (1) Timber collection is a rectangular type of wood which is on average one piece of wood is equal to, Length = 7 feet width = 2, and Height = 1.5 feet. Such types of wood have been used for furniture and construction of houses. Type (2): It is a type of wood that is collected in bundles and is mostly used for fuel energy. The weight of one bundle is equal to 50 kg to 100 kg.
It remained pertinent that the non-existence of safety-nets (e.g., education and technical skills) to ght against exogenous and endogenous shocks has been driving forest behind the studied issues. The exogenous shocks (e.g., natural climates, diseases, and deaths) that imply the need for additional income, have further increased pressure on forest resources to overcome these shocks. Also, poor agricultural output that has been resulting in insu cient income proved to be an exploitive factor.
All this coupled with a lack of management or surveillance by o cial institutions (i.e., forest department KP), caused in people resorting to wood, timber, and NTFPs for obtaining additional cash, food, and energy. The results further established mix relationship between the duration of the HHs' residency and fuelwood used in the study area. Education is found signi cantly negatively related to dependency on forest resources -the higher the education level, desto lower the dependency. The coe cient of education for district Buner, Lower Dir, Malakand, and Upper Dir were : -12.53, -19.17, -10.96, and -1.34 (signi cant at 5% level). While the coe cient of education for district Swat and Shangla remained: -32.63 and -15.24 (at 1% signi cance level). It was related to more sensitivity and awareness towards forests' conservation, environmental health, and the possibility of a diverse pool of job opportunities. The results further re ected an inverse relationship between HH's health condition and fuelwood collection and consumption -the higher the consumption and air pollution desto higher the rate of diseases and illness. Income from NTFP showed a positive and signi cant relationship with forest extraction in districts: Buner and Shangla (coe cient of 0.45 and 0.85), coupled with an insigni cant relationship in districts Lower Dir, Malakand, and Swat (coe cient -0.103, p>0.05). In the case of Upper Dir, the HHs mainly relied on selling forest fruits so an increase in prices led to a decrease in extraction. Here, other sources of income had a positive impact on forest extraction in district Lower Dir (coe cient of 0.168, at 5%.), whereas such impact in districts: Buner, Malakand, Swat, and Upper Dir was positive but statistically insigni cant (p> 5%). Further, the agricultural production (second major income source) had a signi cantly negative impact on forest extraction in all districts as a 1% increase in the technical e ciency of agriculture had decreased HH income from forest extraction. Also, the empirics have revealed that fuelwood prices have a signi cantly negative relationship with forest product extraction in districts Malakand, Shangla, while in districts: Lower Dir, Swat, and Upper Dir the relationship was positive but statistically insigni cant (p>0.05).
Likewise, the results of HHs' access to credit (formal: loans institutions and informal: loans from relatives, neighbors, friends' circle, etc.) established district Buner and Malakand in positive, yet insigni cant relationship with forest products' extraction (p >0.05 and 5% signi cance level). It has demonstrated that more the credit facilities accessible, more the forest products' extraction rate. On the contrary, this relationship was negative (at signi cance level 5% and 1%) in districts: Lower Dir, Shangla, and Upper Dir -more access to credit desto higher deforestation and forest degradation. Lastly, the study found unveiled that in district Buner, HHs' accessibility to the forest has a negative link with forests products extraction ((at 5%), while in district Malakand the said relationship was declared negatively insigni cant (p >0.05). However, in districts: Lower and Upper Dir and Swat, this connection was positively signi cant (at 5%). This shows that as the accessibility to forests increases so the deforestation and deforestation rate, whereas in district Shangla this association was positively insigni cant (p >0.05) (Table 13 a,b).

Discussion
It is established that in the face of poverty and lack of alternative income sources, communities in the vicinity of forests strongly rely upon wood, timber, and NTFPs for subsistence. This in turn reinforces the global trend for forest extraction, because it requires little nancial and physical capacity, but also due to accessibility to generate HH income (Barber et al., 2014;Assuncao et al., 2013a;Maccedo et al. 2012;Hosonuma et al., 2012;Kissinger et al., 2012;Araujo et al., 2010 , (2014). This intense pressure on forest resources was derived from population growth, agricultural expansion, and residential constructions. Yet, poor agricultural output and resultant insu cient income diverted the localities towards un-sustained deforestation and forest degradation. Although more education and health status played an important role in fewer forests (and forest products) extraction as found in other societies con rming the study of Nazir and Olabisi (2015), but low off-farm wages, increased family size, no/limited access to credit, etc. were the factors undermining the positive contribution of education to address the issue.

Conclusion
The state of forests in Pakistan has been under the intense pressure of extraction of forest wood and NTFPs, especially in the highest forest-covered areas of Malakand Division. The rich con ner forests were signi cantly deforested in a period of 17 years. The study con rmed extensive LULCC between 2000-10 at the rate of 0.63% and then 0.87% in 2017 due to fuelwood collection, exaggerated by the hike in prices of forest products, increased demand for fuel and construction wood, illegal extraction, agricultural expansion, population growth, increase in settlements, etc. Other societal factors contributed to the problem were easy accessibility to forests, no/limited access to credit, ine ciency agriculture, lack of alternative energy, and income generation activities. Although, education and degrading health conditions of the forests' dependent communities have decreased this dependency, yet they could not hamper the rate of exploitation.
The state of forests in Pakistan holds contradictive and con icting evidence. The government has taken several steps to address the issue like the development of the National Forest Monitoring System (NFMS), the establishment of sub-national REDD+ units at the provincial level (KP), tree plantation under the Billion Tree Tsunami, the establishment of national parks, green growth initiative, etc. (Baig and Al-Subaiee, 2011). These efforts were to invert the ow of forest dependency through increasing other livelihood prospects and thereupon decreasing poverty. However, the ndings of the independent researches question the execution of all such efforts geared in the protection of forest cover. So, this research study suggests that the concerned public stakeholders provide a supplementary source of energy. As the area has a high potential for biomass gas for which lack of credit and nancial assistance is the main hurdle, the provision of technical knowledge and tools can help the locality to execute village-based biomass gas plants to overcome the local energy needs. In order to meet international agreements such as REDD + forest dependency must be reduced by providing alternative sources of income, where agricultural productivity can be increased through agricultural mechanization on the existing farmland. Lastly, for the conservation of the limited forest resources, property rights should be executed to stop any further deforestation and forest degradation in the study area.

Declarations Ethical Approval
All ethical standard has followed in this research paper. No formal approval is required.

Consent to Participate
The research is not on human and animal subjects.

Consent to Publish
We are willing to publish the research paper in Environmental Science and Pollution Research.

Competing Interests
There is no competing interest in the manuscript.

Availability of data and materials
Data is available from authors on request.

Figure 1
Study area map. Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.

Figure 2
LULC (2000) Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.   city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors. LULCC (2010-2017) Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.