An Evaluation of Bioenergy Industry Sustainability Impacts on Forest Degradation in The EU 28 Region


 The authors have withdrawn this preprint due to author disagreement.

Likewise, it must be noticed that indicators increasing biodiversity also synchronize with those 43 boost other perspectives of sustainable forestry and related to ecological system interests. For 44 instance, removing deadwood, branches, twigs, etc. For example, extracting forestry residues. Also 45 extract a source of soil humus and nutrients, while continual interactions (for bio-wood removal) 46 also increase to degradation of forest soil coverage, enhancing the risk of soil attrition particularly 3 in mounds and heights parts of Europe. Residues removal from forests for bioenergy production 48 by 70 percent of wood wastes and 25 percent of branches contributes to a significant decrease in 49 CO2 fluxes and pertaining humus into the soil especially in coniferous forests (Thran et al., 2017).

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Solid biomass is mainly originating from forestry resources and wood manufacturing sectors. 52 Feedstocks output utilized recently in generating transport bio-fuels or bio-liquids employed in 53 bioelectricity and bio-heat and bio-cool sectors (Lavelle et al., 2011). Precisely, European Union 54 countries committed to supply 20 percent of EU's total energy from sustainable sources on 55 December 31, 2020 and obligated to produce a minimum 27 percent on December 31, 2030. 56 Bioenergy is presently an important contributor of the plan to achieve the related 2020 and 2030 57 goals, and more than 50 percent of sustainable energy in European Union region presently 58 produces from bioenergy industry. Almost 40 percent of the yearly extraction from European 59 Union forests is eventually utilized in bioenergy industry (see Figure 1). Biomass demand raise up to 318 m³ RWE (= round wood equivalent) from natural forests resource 67 for the 2010-2020 period was computed, based on recent estimation to change primary 68 conventional energy output into bio-wood quantities. According to insufficient database of the 69 National Renewable Energy Action Plans (NREAPs), it is ambiguous to which level this 70 increasing consumption will be supplied from EU28 forests resources and which segment could 71 be supplied from imported forestry biomass in EU28 countries. The EU Commission points that 72 the majority of bioenergy output will be produced form extracted wood pellets from forestry 73 resource, with acceleration to be imported from outside the EU (Sikkema et al., 2011). 74 Theoretically, the European Union countries would provide these biomass from forestry resources 75 locally, it is most likely that imports may raise significantly (Alsaleh et al., 2017b). Sikkema et al. 76 (2011) suggests that the European countries consumption for wood pellets will increase threefold 77 by the end of 2020 in compare with the consumption in 2010, and this against the hypothesis of 78 incrementing international consumption. Global trade of biomass from forestry resource is 79 anticipated to expand by the end of 2020 to scales between 5 or 14 times in compare with the scale 80 of 2010 (see Figure 2).

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Expanding the consumption of forest residues for energy production' has obtained high value in 83 energy market. The European countries strategy to generate more than 50% of its sustainable 84 energy from biomass by the end of 2020, which is more than 10% of its total energy consumption 85 (Alsaleh et al., 2017a). Although the majority is produced in forests within the EU region, import 86 of wood pellets from overseas to Europe is considered significant supplier and will expand in the 87 5 future (Sikkim et al, 2011and Hewitt, 2011 (Hewitt, 2011). This raised of European consumption for 90 biomass in 2020 dangers to lead for an additional escalation of deforestation and forest degradation 91 worldwide.  The consumption of biomass for bioenergy output from the waste and residues of forestry resource, 97 add up a further pressure on natural resource. In the case that no adequate cropland for bioenergy 98 production is accessible, this may lead to food crop change (Al-Riffai et al., 2010). As biomass 99 output for stable bioenergy production is highly considered, the most important impact of the 100 timber biomass for bioenergy sustainability is on the condition of forests. Forests can be exposed 101 to lose a great part of their timber and remain be considered as forest, although not as deforested,      The "Fuelwood Gap Hypothesis", established in the 1970s, suggested that wood fuels were used 155 without awareness foundation. The "Gap Hypothesis" pointed that in several nations demand was 156 higher than the sustainable output from forestry areas. The hypothesis derived that deforestation controlling the deforestation level, as Samuelson (1981) suggested that an enhance in the cost of 174 biofuel gives a indication to the suppliers that more biofuel is required and this lead to additional 175 harvesting from forestry resource, which accordingly cause deforestation and environmental 176 10 destruction. All these researches claimed that timber harvesting or use to produce energy would 177 lead to an opposite impact on deforestation, forest destruction or the ecological system.    once, Xt_1 and lagged difference terms. It can include a constant term a and α trend term Yt as 244 presented in Eq (1): where: ∆ is a 1 st difference operative, m is the most advantageous lagged length, i is the time 249 progression, β is parameter assess, α is the stationary parameter and t is the constant random error.

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The examination for a unit root related to the assumption that H0 : β = 0, H1 : β ≠ 0. The status is 251 that at any rate the parameter is insignificant at statistical level, then the assumption that Xt 252 implicate a unit root is declined.  Table   286 1.   The evaluation of the selected sample starts with exploratory examination for the indicators. Table   306 2 shows the descriptive statistics, which claim that the indicators have ordinary allocation. The 307 relationship validation outcomes are presented in Table 3. The validation show no high 308 relationship between the independent determinants, which display nonappearance of multi-  Unit root validation was applied and showed in Table 4. The findings from Levin, Lin and Chu   Table 5).    Panel v-Statistic  institutions may help in mitigating forest destruction due to bioenergy use.    Table 7). Firstly, the authors 392 implemented the VIF examination to give a justification for the absence of multicollinearity issue 393 in Model 2 (VIF = 1.45). Then, the authors applied BPLM examination for Model 2, and the 394 authors discovered that the BPLM examination is significant at the 1 percent statistical scale. This 395 points to the finding that the RE estimator is further suitable than the Pooled OLS estimator 396 because of the individual criteria impacts in the implemented data (see Table 7). Next, he authors 397 implemented a Hausman Fixed examination and the results driving the authors to decline the null 398 hypothesis. This index directs the authors to derive that RE estimator is inappropriate and to 399 discover that the FE estimator peculiarities to be suggested and adopted by LSDVC examination 400 gives a biased evaluation of the coefficients.

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In Table 7, Panel FE estimator was then applied and Model 2 presents its outcome along with the Model 2 result shows that trade openness had a statistically significant and positive impact on the 418 forest degradation at 5% level. Precisely, 1% raise in trade openness will have 0.05% raise in the 419 forestry degradation in EU15 countries. This is align with the a-priori expectation of earlier study 420 Adu and Denkyirah (2017)   Model 3 showed the influence of bioenergy use within intuitional framework on the forest 440 degradation level in the EU19 countries starting from 1990 to 2018 (Table 8) because of the individual criteria influences in the used dataset (see Table 8). Next, the authors 445 implemented a Hausman Fixed examination and the findings leaded the authors to derive that RE 446 model is unsuitable and to conclude that the FE model peculiarities to be considered (see Table 8).   Table 8 shows the finding of the evaluated influence of bioenergy use within institutional goodness 487 framework on forestry destruction in EU13 countries. The findings from both Table 7 and Table   488 8 suggest that bioenergy use under institutional goodness framework has significant negative 489 impact on forest degradation. The findings more denote that the significant negative influence of 490 bioenergy use on forest degradation is greater in EU15 members than in the EU13 members.

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Specifically, the weight of the influence are -0.014 and -0.022 for EU15 and EU13 members,

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respectively. This implies that a remarkable mitigation in forestry destruction can be implemented 493 in EU15 members applying bioenergy use and intuitional quality than in EU13 members. and institutional quality appeared negative and significant coefficients in regards to forestry 508 destruction at EU28, EU15 and EU13 scales. Thus, the effect of bioenergy use on mitigate the 509 forest degradation is greater in EU15 members than in the EU13 members. Adopting new 510 technologies with higher government effectiveness actions in EU13 (underdeveloped) countries 511 will be a handy instrument to achieve higher bioenergy production and mitigate more forest 512 degradation.

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The policy implications of this research were explained as the following. Firstly, since bioenergy 515 use was analytically confirmed to show a negative influence on forestry destruction, decision 516 makers in EU28 countries can emphasize their obligations to supplying sufficient and economical 517 bioenergy output in order to decrease the consumption of biomass wood for energy production.

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Secondly, the institutional quality indicator in EU15 developed members, as revealed in Model 2 519 by the research, may highly decrease forestry destruction. Thus, further effort needs to be allocated 520 for boosting institutional quality in EU13 underdeveloped members to prevent more degradation 521 of forestry lands. Thirdly, as elaborated by the research, the interaction of bioenergy use with 522 efficient institutions should be a magnificent tool for mitigating forestry destruction in the EU28 523 members. Thus, specific sustainability criteria are being adopted beside the modern regulations 524 that lead to a mounted implementations of bioenergy end-uses (including heating/cooling and 525 electricity) due to the promotion of renewable energy in EU28 countries. Last, policy makers in 526 EU13 countries need to create awareness of the economic principle that "cleaner earns, polluter 527 pays" suggests that carbon storage should be subsidised and emissions from forest bioenergy 528 should be fully accounted for and controlled through appropriate means.

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Author's contribution section 532 All three authors contributed to writing, estimation, analysis, and revision of the paper.