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Overview
| Project website | github.com/worldbank/sdg-metadata | |
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| Instructions for translators | This project is limited to Russian translation only, for now. More detailed instructions to come. |
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| Project maintainers |
 brockfanning
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| Translation license | MIT License | |
| Translation process |
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| Source code repository |
https://github.com/worldbank/sdg-metadata
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| Repository branch | master | |
| Last remote commit |
Merge pull request #542 from weblate/weblate-sdg-metadata-1-1-1a
85d59a4a6e9
brockfanning authored 7 months ago |
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| Last commit in Weblate |
Translated using Weblate (Portuguese)
5afc8b49daa
brockfanning authored a month ago |
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| Weblate repository |
https://hosted.weblate.org/git/sdg-metadata/1-1-1a/
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| File mask | translations-metadata/*/15-2-1.yml |
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| Monolingual base language file | translations-metadata/en/15-2-1.yml |
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12 hours ago
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| Source | 32 | 4,583 | 36,137 | |||
| Approved | 0% | 0 | 0% | 0 | 0% | 0 |
| Waiting for review | 4% | 8 | 1% | 163 | 1% | 1,311 |
| Translated | 20% | 40 | 17% | 4,746 | 17% | 37,448 |
| Needs editing | 32% | 63 | 43% | 11,931 | 42% | 93,219 |
| Read-only | 16% | 32 | 16% | 4,583 | 16% | 36,137 |
| Failing checks | 50% | 96 | 63% | 17,496 | 63% | 138,176 |
| Strings with suggestions | 0% | 0 | 0% | 0 | 0% | 0 |
| Untranslated strings | 46% | 89 | 39% | 10,821 | 39% | 86,155 |
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<p><strong>URL: </strong><a href="http://www.fao.org/forest-resources-assessment/en/"><u>http://www.fao.org/forest-resources-assessment/en/</u></a> </p>
<p><strong>References:</strong></p> <p>Global Forest Resources Assessment 2020, Guidelines and Specifications (<a href="http://www.fao.org/3/I8699EN/i8699en.pdf"><u>www.fao.org/3/I8699EN/i8699en.pdf</u></a>)</p> <p>Global Forest Resources Assessment 2020, Terms and Definitions (<a href="http://www.fao.org/3/I8661EN/i8661en.pdf"><u>www.fao.org/3/I8661EN/i8661en.pdf</u></a>).</p> <p>United Nations. Resolution adopted by the General Assembly on 17 December 2007 (<a href="https://undocs.org/en/A/RES/62/98">https://undocs.org/en/A/RES/62/98</a>). </p> <p><strong>Annex 1 – Terms and Definitions </strong></p> <p><strong>FOREST</strong></p> <p>Land spanning more than 0.5 hectares with <u>tree</u>s higher than 5 meters and a <u>canopy cover</u> of more than 10 percent, or trees able to reach these thresholds <em>in situ</em>. It does not include land that is predominantly under agricultural or urban land use. </p> <p><u>Explanatory notes</u></p> <ol> <li>Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters. </li> <li>Includes areas with young trees that have not yet reached but which are expected to reach a canopy cover of at least 10 percent and tree height of 5 meters or more. It also includes areas that are temporarily unstocked due to clear-cutting as part of a forest management practice or natural disasters, and which are expected to be regenerated within 5 years. Local conditions may, in exceptional cases, justify that a longer time frame is used.</li> <li>Includes forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific environmental, scientific, historical, cultural or spiritual interest.</li> <li>Includes windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectares and width of more than 20 meters.</li> <li>Includes abandoned shifting cultivation land with a regeneration of trees that have, or are expected to reach, a canopy cover of at least 10 percent and tree height of at least 5 meters.</li> <li>Includes areas with mangroves in tidal zones, regardless whether this area is classified as land area or not.</li> <li>Includes rubberwood, cork oak and Christmas tree plantations. </li> <li>Includes areas with bamboo and palms provided that land use, height and canopy cover criteria are met.</li> <li><u>Excludes</u> tree stands in agricultural production systems, such as fruit tree plantations, oil palm plantations, olive orchards and agroforestry systems when crops are grown under tree cover. <u>Note:</u> Some agroforestry systems such as the “Taungya” system where crops are grown only during the first years of the forest rotation should be classified as forest.</li> </ol> <p><strong>ABOVE-GROUND BIOMASS</strong></p> <p>All living biomass above the soil including stem, stump, branches, bark, seeds, and foliage.</p> <p><u>Explanatory note </u></p> <ol> <li>In cases where forest understorey is a relatively small component of the aboveground biomass carbon pool, it is acceptable to exclude it, provided this is done in a consistent manner throughout the inventory time series.</li> </ol> <p><strong>PROTECTED AREAS</strong></p> <p>Areas especially dedicated to the protection and maintenance of biological diversity, and of natural and associated cultural resources, and managed through legal or other effective means.</p> <p><strong>FOREST AREA WITHIN PROTECTED AREAS</strong></p> <p>Forest area within formally established protected areas independently of the purpose for which the protected areas were established. </p> <p><u>Explanatory notes</u></p> <ol> <li>Includes IUCN Categories I – IV</li> <li><u>Excludes</u> IUCN Categories V-VI</li> </ol> <p><strong>FOREST AREA WITH MANAGEMENT PLAN</strong></p> <p>Forest area that has a long-term documented management plan, aiming at defined management goals, which is periodically revised. </p> <p><u>Explanatory notes</u></p> <ol> <li>A forest area with management plan may refer to forest management unit level or aggregated forest management unit level (forest blocks, farms, enterprises, watersheds, municipalities, or wider units).</li> <li>A management plan must include adequate detail on operations planned for individual operational units (stands or compartments) but may also provide general strategies and activities planned to reach management goals.</li> <li>Includes forest area in protected areas with management plan.</li> </ol> <p><strong>INDEPENDENTLY VERIFIED FOREST MANAGEMENT CERTIFICATION</strong></p> <p>Forest area certified under a forest management certification scheme with published standards and is independently verified by a third-party.</p> <p><strong>Annex 2 – Methodology</strong></p> <p><strong>Sub-indicator 1 - Annual forest area change rate</strong></p> <p><u>Unit</u>: Percent</p> <p><u>Reference period:</u> 2010-2020</p> <p><u>Method of estimation:</u> Compound annual change rate formula as follows:</p> <p><math xmlns="http://www.w3.org/1998/Math/MathML"> <mi>r</mi> <mo>=</mo> <mfenced open="[" close="]" separators="|"> <mrow> <msup> <mrow> <mfenced separators="|"> <mrow> <mfrac> <mrow> <msub> <mrow> <mi>A</mi> <mi>F</mi> </mrow> <mrow> <mi>t</mi> <mn>2</mn> </mrow> </msub> </mrow> <mrow> <msub> <mrow> <mi>A</mi> <mi>F</mi> </mrow> <mrow> <mi>t</mi> <mn>1</mn> </mrow> </msub> </mrow> </mfrac> </mrow> </mfenced> </mrow> <mrow> <mfrac bevelled="true"> <mrow> <mn>1</mn> </mrow> <mrow> <mfenced separators="|"> <mrow> <mi>t</mi> <mn>2</mn> <mo>-</mo> <mi>t</mi> <mn>1</mn> </mrow> </mfenced> </mrow> </mfrac> </mrow> </msup> <mo>-</mo> <mn>1</mn> </mrow> </mfenced> <mo>×</mo> <mn>100</mn> </math></p> <p>where: </p> <p><em>r</em> = compound annual change rate for the period <em>t<sub>1 - </sub>t<sub>2</sub></em><sub> </sub></p> <p><em>t<sub>i</sub></em> = time i (year)</p> <p><em>AF<sub>t1</sub></em> = forest area at <em>t<sub>1</sub></em> </p> <p><em>AF<sub>t2</sub></em> = forest area at <em>t<sub>2</sub></em></p> <p><u>Translation to dashboard/traffic light:</u></p> <p>The following flowchart explains the logic behind the translation of this indicator to a dashboard/traffic light:</p> <p>Forest area change direction</p> <p>Forest area stable </p> <p>or increasing</p> <p>Forest area decreasing</p> <p>Change in forest area loss rate </p> <p>Loss rate</p> <p>decreasing</p> <p>Loss rate stable</p> <p>or increasing</p> <p>The forest area change direction is determined by examining the value of the forest area change rate for the most recent period, a negative value indicate a loss of forest area, a zero value means that forest area is stable, and a positive value means that forest area has increased. The change in forest area loss rate<sup><a href="#footnote-2" id="footnote-ref-2">[1]</a></sup> is based on a comparison of the annual forest area change rate for the period 2010-2020 with the annual <u>forest area change rate for the period 2000-2010 </u>(<u>baseline).</u></p> <p><u>Comments:</u></p> <p>This traffic light takes into consideration both the direction of forest area change (if forest area increases or decreases) as well as changes in the rate of forest area loss – the latter important in order to indicate progress among countries that are losing forest area but manage to reduce the loss rate. </p> <p>The baseline should be updated every 5 years. In 2020 a new baseline was calculated for the period 2000-2010 based on updated country data. </p> <p><strong>Sub-indicator 2 – Above-ground biomass in forest </strong></p> <p><u>Unit</u>: tonnes/hectare</p> <p><u>Reference year:</u> Latest reporting year</p> <p><u>Method of estimation:</u> Reported directly by countries</p> <p><u>Translation to dashboard/traffic light:</u></p> <p>The indicator value for the latest reporting year is compared with the indicator value reported for 2010.</p> <p>The ratio (r) between the current indicator value and the value reported for 2010 is calculated; r>1 means an increase in stock per hectare, r<1 means a decrease while 1 indicates no change. A narrow interval for r has been established to indicate a stable condition, and traffic-light colors are assigned as follows:</p> <p> r ≥ 1.01 </p> <p> 0.99 < r < 1.01</p> <p> r ≤ 0.99</p> <p><strong>Sub-indicator 3 – Proportion of forest area within legally established protected areas.</strong></p> <p><u>Unit</u>: Percent</p> <p><u>Reference year:</u> Latest reporting year</p> <p><u>Method of estimation:</u> </p> <p><math xmlns="http://www.w3.org/1998/Math/MathML"> <mi>r</mi> <mo>=</mo> <mi>&nbsp;</mi> <mfrac> <mrow> <msub> <mrow> <mi>A</mi> <mi>F</mi> <mi>P</mi> </mrow> <mrow> <mfenced open="[" close="]" separators="|"> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> <mi>e</mi> <mi>r</mi> <mi>e</mi> <mi>n</mi> <mi>c</mi> <mi>e</mi> <mi>&nbsp;</mi> <mi>y</mi> <mi>e</mi> <mi>a</mi> <mi>r</mi> </mrow> </mfenced> </mrow> </msub> </mrow> <mrow> <msub> <mrow> <mi>A</mi> <mi>F</mi> </mrow> <mrow> <mn>2015</mn> </mrow> </msub> </mrow> </mfrac> <mo>×</mo> <mn>100</mn> </math></p> <p>Where:</p> <p><em>AFP</em> = Forest area within legally established protected areas</p> <p><em>AF</em> = Total forest area</p> <p><u>Translation to dashboard/traffic light:</u></p> <p>The indicator value for latest reporting year is compared with the indicator value reported for 2010.</p> <p>The ratio (r) between the current indicator value and the value reported for 2010 is calculated; r>1 means an increase in forest area within protected areas, r<1 means a decrease while 1 indicates no change. A narrow interval for r has been established to indicate a stable condition, and traffic-light colors are assigned as follows:</p> <p> r ≥ 1.01 </p> <p> 0.99 < r < 1.01</p> <p> r ≤ 0.99</p> <p><u>Comment:</u></p> <p>Using forest area in 2015 as denominator for estimating this indicator ensures that the time series of percentages reflect real changes in the forest area within legally established protected areas and is not affected by changes (losses or gains) in total forest area. </p> <p><strong>Sub-indicator 4 – Proportion of forest area under a long-term management plan.</strong></p> <p><u>Unit</u>: Percent</p> <p><u>Reference year:</u> Latest reporting year</p> <p><u>Method of estimation:</u> </p> <p><math xmlns="http://www.w3.org/1998/Math/MathML"> <mi>r</mi> <mo>=</mo> <mi>&nbsp;</mi> <mfrac> <mrow> <msub> <mrow> <mi>A</mi> <mi>F</mi> <mi>M</mi> <mi>P</mi> </mrow> <mrow> <mfenced open="[" close="]" separators="|"> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> <mi>e</mi> <mi>r</mi> <mi>e</mi> <mi>n</mi> <mi>c</mi> <mi>e</mi> <mi>&nbsp;</mi> <mi>y</mi> <mi>e</mi> <mi>a</mi> <mi>r</mi> </mrow> </mfenced> </mrow> </msub> </mrow> <mrow> <msub> <mrow> <mi>A</mi> <mi>F</mi> </mrow> <mrow> <mn>2015</mn> </mrow> </msub> </mrow> </mfrac> <mo>×</mo> <mn>100</mn> </math></p> <p>Where:</p> <p><em>AFMP</em> = Forest area under a long-term management plan</p> <p><em>AF</em> = Total forest area</p> <p><u>Translation to dashboard/traffic light: </u>The indicator value for latest reporting year is compared with the indicator value reported for 2010.</p> <p>The ratio (r) between the current indicator value and the value reported for 2010 is calculated; r>1 means an increase in areas under management plan, r<1 means a decrease while 1 indicates no change. A narrow interval for r has been established to indicate a stable condition, and traffic-light colors are assigned as follows:</p> <p> r ≥ 1.01 </p> <p> 0.99 < r < 1.01</p> <p> r ≤ 0.99</p> <p><u>Comment:</u></p> <p>Using forest area in 2015 as denominator for estimating this indicator ensures that the time series of percentages reflect real changes in the forest area under management plan and is not affected by changes (losses or gains) in total forest area. </p> <p><strong>Sub-indicator 5 – Forest area under an independently verified forest management certification scheme.</strong></p> <p><u>Unit</u>: Thousand hectares</p> <p><u>Reference year:</u> Latest reporting year (as of June 30)</p> <p><u>Method of estimation:</u> Data is collected directly from the databases of each certification scheme and provided to countries for validation.</p> <p><u>Translation to dashboard/traffic light: </u>The indicator value for latest reporting year is compared with the indicator value for previous reporting year for assessment of continuity of progress since last report.</p> <p>The ratio (r) between the current indicator value and the previously reported value is calculated; r>1 means an increase in areas under an independent forest management certification scheme, r<1 means a decrease while 1 indicates no change. A small interval for r has been established to indicate a stable condition, and traffic-light colors are assigned as follows:</p> <p> r ≥ 1.01 </p> <p> 0.99 < r < 1.01</p> <p> r ≤ 0.99</p> <p><u>Comments:</u></p> <p>Using June 30 as the date for reporting, allows for the certification bodies to have their databases updated so they can provide information to FAO by end of the year, and then be included in the annual reporting to SDG in the beginning of the following year.</p><div class="footnotes"><div><sup class="footnote-number" id="footnote-2">1</sup><p> If forest area change rate is negative (= forest loss) then: annual forest area loss rate = <strong>-</strong> (annual forest area change rate) <a href="#footnote-ref-2">↑</a></p></div></div>
<h1>Ссылки </h1>
<h2>URL: </h2> <p><a href="http://www.fao.org/forest-resources-assessment/en/">http://www.fao.org/forest-resources-assessment/en/</a> </p> <h2>Рекомендации:</h2> <p><a href="http://www.fao.org/forest-resources-assessment/current-assessment/en/">http://www.fao.org/forest-resources-assessment/current-assessment/en/</a></p> <h1>Приложение 1 Обозначения и определения<sup><a href="#footnote-1" id="footnote-ref-1">[1]</a></sup></h1> <p><strong><em>ЛЕС</em></strong></p> <p>Участок земли площадью более 0,5 гектара, на котором растут деревья высотой более пяти метров с сомкнутостью крон более десяти процентов, или деревья, способные на данном участке достичь этих пороговых значений. Не включаются земли, которые используются преимущественно для целей сельского хозяйства или поселений<strong>. </strong></p> <p><u>Пояснительные примечания</u></p> <ol> <li>Лес определяется как наличием деревьев, так и отсутствием других преобладающих видов землепользования. Деревья должны быть способны достигать минимальной высоты 5 м. </li> <li>Сюда входят участки с молодыми деревьями, с сомкнутостью крон еще не достигшей, но которая, как ожидается, достигнет 10 %, а деревья – высоты 5 метров. Он также включает участки, которые являются временно обезлесенными из-за сплошных рубок в рамках практики ведения лесного хозяйства или из-за стихийных бедствий, но которые, как ожидается, будут восстановлены в течение 5 лет. В исключительных случаях, с учетом местных условий, могут устанавливаться более продолжительные временные сроки.</li> <li>Лес включает лесные дороги, противопожарные полосы и другие небольшие открытые пространства, леса в национальных парках, природных заповедниках и на других охраняемых территориях, представляющих особый экологический, научный, исторический, культурный или духовный интерес.</li> <li>Лес включает ветрозащитные полосы, лесозащитные полосы и полосы деревьев площадью более 0,5 га и шириной более 20 м.</li> <li>Сюда входят заброшенные земли со сменной культивацией, где производится восстановление лесонасаждений, которые имеют или, как ожидается, будут иметь сомкнутость крон 10 %, а деревья – высоту 5 метров.</li> <li>Лес включает площади, занятые мангровыми лесами в приливных зонах, независимо от того, классифицируются ли эти площади в качестве земельных площадей.</li> <li>Лес включает плантации каучуковых деревьев, пробкового дуба и рождественских елок. </li> <liСюда входят площади, занятые бамбуковыми и пальмовыми лесами при условии, что землепользование, высота деревьев и сомкнутость крон соответствуют установленным критериям.</li> <li>Лес не включает древесные насаждения, вовлеченные в сельскохозяйственное производство, такие как плантации плодовых деревьев, плантации масличной пальмы, оливковые рощи и агролесоводственные системы, при которых сельскохозяйственные культуры выращиваются под кронами деревьев. <u>Примечание:</u>Площади, включенные в некоторые агролесоводственные системы, такие как система Taungya, при которых сельскохозяйственные культуры выращиваются только в первые годы оборота лесного хозяйства, должны рассматриваться как лес.</li> </ol> <p><strong><em>НАДПОЧВЕННАЯ БИОМАССА</em></strong></p> <p>Вся биомасса живого растительного покрова, как древесного, так и травянистого, включая стволы, пни, ветви, кору, семена и листву.</p> <p><u>Пояснительные примечания </u></p> <ol> <li>В случаях, когда подрост является относительно незначительным компонентом пула углерода в надпочвенной биомассе, его допустимо исключить при условии, что это будет осуществляться последовательно в ходе инвентаризаций в отчетные годы.</li> </ol> <p><strong><em>ОХРАНЯЕМЫЕ ТЕРРИТОРИИ</em></strong></p> <p>Территории, специально предназначенные для защиты и поддержания биологического разнообразия, природных и связанных с ними культурных ресурсов и управляемые законными или другими эффективными способами.</p> <p><strong><em>ЛЕСНЫЕ ПЛОЩАДИ НА ЗАКОННО УСТАНОВЛЕННЫХ ОХРАНЯЕМЫХ ТЕРРИТОРИЯХ</em></strong></p> <p>Лесная площадь на официально созданных охраняемых территориях, независимо от цели, с которой были образованы охраняемые территории. </p> <p><u>Пояснительные примечания</u></p> <ol> <li>Включает категории МСОП I – IV</li> <li>Исключает категории МСОП V – VI</li> </ol> <p><strong><em>ЛЕСНАЯ ПЛОЩАДЬ С ДОЛГОСРОЧНЫМ ПЛАНОМ УПРАВЛЕНИЯ ЛЕСАМИ</em></strong></p> <p>Лесная площадь, имеющая долгосрочный документированный план управления лесами, направленный на достижение определенных целей управления, и периодически пересматривающийся.</p> <p><u>Пояснительные примечания</u></p> <ol> <li>Лесная площадь с планом управления лесами может означать уровень лесоустроительного подразделения или агрегированный уровень лесоустроительного подразделения (лесные участки, фермы, предприятия, водосборные бассейны, муниципальные образования или более крупные участки).</li> <li>План управления лесами может включать подробную информацию об операциях, запланированных для отдельных эксплуатационных единиц (насаждений или кварталов), но он также может ограничиваться предоставлением общих стратегий и мероприятий, запланированных для достижения целей в области управления лесами.</li> <li>Включает лесную площадь на охраняемых территориях с планом управления лесами.</li> </ol> <p><strong><em>СИСТЕМА СЕРТИФИКАЦИИ ИСПОЛЬЗОВАНИЯ ЛЕСОВ, ПРОШЕДШАЯ НЕЗАВИСИМУЮ ПРОВЕРКУ</em></strong></p> <p>Лесные территории, сертифицированные по схеме сертификации управления лесами с опубликованными стандартами и прошедшие независимую проверку третьей стороной.</p> <h1>Приложение 2 Методология</h1> <p><strong><em>Субпоказатель 1 - Темп годового чистого изменения площади лесов</em></strong></p> <p><u>Единица измерения</u>: Процент</p> <p><u>Отчетный период:</u> Самый последний период</p> <p><u>Метод оценки:</u> Формула сложных процентов</p> <p><u>Перевод на инструментальную панель / световые сигналы:</u></p> <p>Следующая блок-схема объясняет логику перевода этого показателя на инструментальную панель / световые сигналы:</p> <p><img src="data:image/png;base64,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"></p> <p>Направление изменения площади лесов определяется путем изучения значения скорости изменения площади лесов за последний период, отрицательное значение указывает на потерю площади лесов, нулевое значение означает, что площадь лесов стабильна, а положительное значение означает, что площадь лесов возросла. Изменение скорости потери площади лесов основано на сравнении текущей скорости чистого изменения площади лесов с базовой скоростью чистого изменения площади лесов за период 2010-2015 гг.</u></p> <p><u>Комментарии:</u></p> <p>Этот световой сигнал учитывает как направление изменения площади лесов (если площадь лесов увеличивается или уменьшается), так и изменения скорости потери площади лесов; последнее важно для определения прогресса среди стран, которые теряют площади лесов, но сумели снизить скорость потерь.</p> <p>Для годовой отчетности ФАО может предоставлять странам условно исчисленные значения на основе предыдущих тенденций, которые они могут использовать в случае, если у них нет новой / обновленной информации. Базовый уровень должен обновляться каждые 5 лет, поэтому в 2020 году рассчитывается новый базовый уровень. Кроме того, на страновом уровне, если страна получает новую информацию и обновляет исторические временные ряды, базовый уровень для страны будет пересчитан с учетом периода 2010-2015 гг.</p> <p><strong><em>Субпоказатель 2 – Запасы наземной лесной биомассы</em></strong></p> <p><u>Единица измерения</u>: тонна/гектар</p> <p><u>Отчетный период:</u> Самый последний период</p> <p><u>Метод оценки:</u> Запас лесной биомассы (в тоннах) / лесная территория (в гектарах)</p> <p><u>Перевод на инструментальную панель / световые сигналы:</u></p> <p>Значение показателя за последний отчетный год сравнивается со значением показателя за предыдущий отчетный год для оценки непрерывности прогресса с момента последнего отчета.</p> <p>Рассчитывается соотношение (r) между текущим значением показателя и ранее отраженным значением; r & gt; 1 означает увеличение запасов на гектар, r & lt; 1 означает уменьшение, а 1 означает отсутствие изменений. Был установлен узкий интервал для r, чтобы указать стабильное состояние, и цвета светового сигнала обозначаются следующим образом:</p> <p><img src="data:image/png;base64,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"></p> <p><strong><em>Субпоказатель 3 –Доля лесных площадей, расположенных в пределах установленных законом охраняемых территорий</em></strong></p> <p><u>Единица измерения</u>: Процент</p> <p><u>Отчетный период:</u> Самый последний период</p> <p><u>Метод оценки:</u> Доля лесных площадей, расположенных в пределах установленных законом охраняемых территорий / Лесная площадь в 2015 году * 100</p> <p><u>Перевод на инструментальную панель / световые сигналы:</u></p> <p>Значение показателя за последний отчетный год сравнивается со значением показателя за предыдущий отчетный год для оценки непрерывности прогресса с момента последнего отчета.</p> <p>Рассчитывается соотношение (r) между текущим значением показателя и ранее отраженным значением; r & gt; 1 означает увеличение запасов на гектар, r & lt; 1 означает уменьшение, а 1 означает отсутствие изменений. Был установлен узкий интервал для r, чтобы указать стабильное состояние, и цвета светового сигнала обозначаются следующим образом:</p> <p><img src="data:image/png;base64,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"></p> <p><u>Комментарий:</u></p> <p>Использование площади лесов в 2015 году в качестве знаменателя для оценки этого показателя гарантирует, что временной ряд в процентах отражает реальные изменения площади лесов в пределах установленных законом охраняемых территорий и не зависит от изменений (потерь или прибылей) в общей площади лесов.</p> <p><strong><em>Субпоказатель 4 – Доля лесных площадей, имеющих долгосрочный план управления лесами.</em></strong></p> <p><u>Единица измерения</u>: Процент</p> <p><u>Отчетный период:</u> Самый последний период</p> <p><u>Метод оценки:</u> Лесные площади, имеющие долгосрочный план управления лесами / Лесная площадь в 2015 году * 100</p> <p><u>Перевод на инструментальную панель / световые сигналы: </u>Значение показателя за последний отчетный год сравнивается со значением показателя за предыдущий отчетный год для оценки непрерывности прогресса с момента последнего отчета.</p> <p>Рассчитывается соотношение (r) между текущим значением показателя и ранее отраженным значением; r & gt; 1 означает увеличение запасов на гектар, r & lt; 1 означает уменьшение, а 1 означает отсутствие изменений. Был установлен узкий интервал для r, чтобы указать стабильное состояние, и цвета светового сигнала обозначаются следующим образом:</p> <p><img src="data:image/png;base64,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"></p> <p><u>Комментарий:</u></p> <p>Использование площади лесов в 2015 году в качестве знаменателя для оценки этого показателя гарантирует, что временной ряд в процентах отражает реальные изменения площади лесов, имеющих долгосрочный план управления лесами, и не зависит от изменений (потерь или прибылей) в общей площади лесов. </p> <p><strong><em>Субпоказатель 5 – Площадь лесов, на которых действует система сертификации использования лесов, прошедшая независимую проверку.</em></strong></p> <p><u>Единица измерения</u>: Тысяч гектаров</p> <p><u>Отчетный период:</u> Самый последний период (по состоянию на 30 июня)</p> <p><u>Метод оценки:</u> Данные собираются непосредственно из баз данных каждой системы сертификации и предоставляются странам для проверки.</p> <p><u>Перевод на инструментальную панель / световые сигналы: </u>Значение показателя за последний отчетный год сравнивается со значением показателя за предыдущий отчетный год для оценки непрерывности прогресса с момента последнего отчета.</p> <p>Рассчитывается соотношение (r) между текущим значением показателя и ранее отраженным значением; r & gt; 1 означает увеличение запасов на гектар, r & lt; 1 означает уменьшение, а 1 означает отсутствие изменений. Был установлен узкий интервал для r, чтобы указать стабильное состояние, и цвета светового сигнала обозначаются следующим образом:</p> <p><img src="data:image/png;base64,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"> </p> <p><u>Комментарии:</u></p> <p>Использование 30 июня в качестве отчетной даты позволяет органам по сертификации обновлять свои базы данных, чтобы они могли предоставлять информацию в ФАО к концу года, а затем включать ее в годовую отчетность для ЦУР в начале следующего года.</p><div class="footnotes"><div><sup class="footnote-number" id="footnote-1">1</sup><p> Глобальная оценка лесных ресурсов 2015 г. – Обозначения и определения <a href="http://www.fao.org/docrep/017/ap862e:/www.fo.org/docrep/017/ap862e/ap862e00.pdf</a> <a href="#footnote-ref-1">↑</a></p></div></div> |
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<p><strong>URL: </strong><a href="http://www.fao.org/forest-resources-assessment/en/"><u>http://www.fao.org/forest-resources-assessment/en/</u></a> </p>
<p><strong>References:</strong></p> <p>Global Forest Resources Assessment 2020, Terms and Definitions (<a href="http://www.fao.org/3/I8661EN/i8661en.pdf"><u>www.fao.org/3/I8661EN/i8661en.pdf</u></a>).</p> <p>United Nations. Resolution adopted by the General Assembly on 17 December 2007 (<a href="https://undocs.org/en/A/RES/62/98">https://undocs.org/en/A/RES/62/98</a>). </p> <p><strong>Annex 1 – Terms and Definitions </strong></p> <p><strong>FOREST</strong></p> <p>Land spanning more than 0.5 hectares with <u>tree</u>s higher than 5 meters and a <u>canopy cover</u> of more than 10 percent, or trees able to reach these thresholds <em>in situ</em>. It does not include land that is predominantly under agricultural or urban land use. </p> <p><u>Explanatory notes</u></p> <ol> <li>Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters. </li> <li>Includes areas with young trees that have not yet reached but which are expected to reach a canopy cover of at least 10 percent and tree height of 5 meters or more. It also includes areas that are temporarily unstocked due to clear-cutting as part of a forest management practice or natural disasters, and which are expected to be regenerated within 5 years. Local conditions may, in exceptional cases, justify that a longer time frame is used.</li> <li>Includes forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific environmental, scientific, historical, cultural or spiritual interest.</li> <li>Includes windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectares and width of more than 20 meters.</li> <li>Includes abandoned shifting cultivation land with a regeneration of trees that have, or are expected to reach, a canopy cover of at least 10 percent and tree height of at least 5 meters.</li> <li>Includes areas with mangroves in tidal zones, regardless whether this area is classified as land area or not.</li> <li>Includes rubberwood, cork oak and Christmas tree plantations. </li> <li>Includes areas with bamboo and palms provided that land use, height and canopy cover criteria are met.</li> <li><u>Excludes</u> tree stands in agricultural production systems, such as fruit tree plantations, oil palm plantations, olive orchards and agroforestry systems when crops are grown under tree cover. <u>Note:</u> Some agroforestry systems such as the “Taungya” system where crops are grown only during the first years of the forest rotation should be classified as forest.</li> </ol> <p><strong>ABOVE-GROUND BIOMASS</strong></p> <p>All living biomass above the soil including stem, stump, branches, bark, seeds, and foliage.</p> <p><u>Explanatory note </u></p> <ol> <li>In cases where forest understorey is a relatively small component of the aboveground biomass carbon pool, it is acceptable to exclude it, provided this is done in a consistent manner throughout the inventory time series.</li> </ol> <p><strong>PROTECTED AREAS</strong></p> <p>Areas especially dedicated to the protection and maintenance of biological diversity, and of natural and associated cultural resources, and managed through legal or other effective means.</p> <p><strong>FOREST AREA WITHIN PROTECTED AREAS</strong></p> <p>Forest area within formally established protected areas independently of the purpose for which the protected areas were established. </p> <p><u>Explanatory notes</u></p> <ol> <li>Includes IUCN Categories I – IV</li> <li><u>Excludes</u> IUCN Categories V-VI</li> </ol> <p><strong>FOREST AREA WITH MANAGEMENT PLAN</strong></p> <p>Forest area that has a long-term documented management plan, aiming at defined management goals, which is periodically revised. </p> <p><u>Explanatory notes</u></p> <ol> <li>A forest area with management plan may refer to forest management unit level or aggregated forest management unit level (forest blocks, farms, enterprises, watersheds, municipalities, or wider units).</li> <li>A management plan must include adequate detail on operations planned for individual operational units (stands or compartments) but may also provide general strategies and activities planned to reach management goals.</li> <li>Includes forest area in protected areas with management plan.</li> </ol> <p><strong>INDEPENDENTLY VERIFIED FOREST MANAGEMENT CERTIFICATION</strong></p> <p>Forest area certified under a forest management certification scheme with published standards and is independently verified by a third-party.</p> <p><strong>Annex 2 – Methodology</strong></p> <p><strong>Sub-indicator 1 - Annual forest area change rate</strong></p> <p><u>Unit</u>: Percent</p> <p><u>Reference period:</u> 2010-2020</p> <p><u>Method of estimation:</u> Compound annual change rate formula as follows:</p> <p><math xmlns="http://www.w3.org/1998/Math/MathML"> <mi>r</mi> <mo>=</mo> <mfenced open="[" close="]" separators="|"> <mrow> <msup> <mrow> <mfenced separators="|"> <mrow> <mfrac> <mrow> <msub> <mrow> <mi>A</mi> <mi>F</mi> </mrow> <mrow> <mi>t</mi> <mn>2</mn> </mrow> </msub> </mrow> <mrow> <msub> <mrow> <mi>A</mi> <mi>F</mi> </mrow> <mrow> <mi>t</mi> <mn>1</mn> </mrow> </msub> </mrow> </mfrac> </mrow> </mfenced> </mrow> <mrow> <mfrac bevelled="true"> <mrow> <mn>1</mn> </mrow> <mrow> <mfenced separators="|"> <mrow> <mi>t</mi> <mn>2</mn> <mo>-</mo> <mi>t</mi> <mn>1</mn> </mrow> </mfenced> </mrow> </mfrac> </mrow> </msup> <mo>-</mo> <mn>1</mn> </mrow> </mfenced> <mo>×</mo> <mn>100</mn> </math></p> <p>where: </p> <p><em>r</em> = compound annual change rate for the period <em>t<sub>1 - </sub>t<sub>2</sub></em><sub> </sub></p> <p><em>t<sub>i</sub></em> = time i (year)</p> <p><em>AF<sub>t1</sub></em> = forest area at <em>t<sub>1</sub></em> </p> <p><em>AF<sub>t2</sub></em> = forest area at <em>t<sub>2</sub></em></p> <p><u>Translation to dashboard/traffic light:</u></p> <p>The following flowchart explains the logic behind the translation of this indicator to a dashboard/traffic light:</p> <p>Forest area change direction</p> <p>Forest area stable </p> <p>or increasing</p> <p>Forest area decreasing</p> <p>Change in forest area loss rate </p> <p>Loss rate</p> <p>decreasing</p> <p>Loss rate stable</p> <p>or increasing</p> <p>The forest area change direction is determined by examining the value of the forest area change rate for the most recent period, a negative value indicate a loss of forest area, a zero value means that forest area is stable, and a positive value means that forest area has increased. The change in forest area loss rate<sup><a href="#footnote-2" id="footnote-ref-2">[1]</a></sup> is based on a comparison of the annual forest area change rate for the period 2010-2020 with the annual <u>forest area change rate for the period 2000-2010 </u>(<u>baseline).</u></p> <p><u>Comments:</u></p> <p>This traffic light takes into consideration both the direction of forest area change (if forest area increases or decreases) as well as changes in the rate of forest area loss – the latter important in order to indicate progress among countries that are losing forest area but manage to reduce the loss rate. </p> <p>The baseline should be updated every 5 years. In 2020 a new baseline was calculated for the period 2000-2010 based on updated country data. </p> <p><strong>Sub-indicator 2 – Above-ground biomass in forest </strong></p> <p><u>Unit</u>: tonnes/hectare</p> <p><u>Reference year:</u> Latest reporting year</p> <p><u>Method of estimation:</u> Reported directly by countries</p> <p><u>Translation to dashboard/traffic light:</u></p> <p>The indicator value for the latest reporting year is compared with the indicator value reported for 2010.</p> <p>The ratio (r) between the current indicator value and the value reported for 2010 is calculated; r>1 means an increase in stock per hectare, r<1 means a decrease while 1 indicates no change. A narrow interval for r has been established to indicate a stable condition, and traffic-light colors are assigned as follows:</p> <p> r ≥ 1.01 </p> <p> 0.99 < r < 1.01</p> <p> r ≤ 0.99</p> <p><strong>Sub-indicator 3 – Proportion of forest area within legally established protected areas.</strong></p> <p><u>Unit</u>: Percent</p> <p><u>Reference year:</u> Latest reporting year</p> <p><u>Method of estimation:</u> </p> <p><math xmlns="http://www.w3.org/1998/Math/MathML"> <mi>r</mi> <mo>=</mo> <mi>&nbsp;</mi> <mfrac> <mrow> <msub> <mrow> <mi>A</mi> <mi>F</mi> <mi>P</mi> </mrow> <mrow> <mfenced open="[" close="]" separators="|"> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> <mi>e</mi> <mi>r</mi> <mi>e</mi> <mi>n</mi> <mi>c</mi> <mi>e</mi> <mi>&nbsp;</mi> <mi>y</mi> <mi>e</mi> <mi>a</mi> <mi>r</mi> </mrow> </mfenced> </mrow> </msub> </mrow> <mrow> <msub> <mrow> <mi>A</mi> <mi>F</mi> </mrow> <mrow> <mn>2015</mn> </mrow> </msub> </mrow> </mfrac> <mo>×</mo> <mn>100</mn> </math></p> <p>Where:</p> <p><em>AFP</em> = Forest area within legally established protected areas</p> <p><em>AF</em> = Total forest area</p> <p><u>Translation to dashboard/traffic light:</u></p> <p>The indicator value for latest reporting year is compared with the indicator value reported for 2010.</p> <p>The ratio (r) between the current indicator value and the value reported for 2010 is calculated; r>1 means an increase in forest area within protected areas, r<1 means a decrease while 1 indicates no change. A narrow interval for r has been established to indicate a stable condition, and traffic-light colors are assigned as follows:</p> <p> r ≥ 1.01 </p> <p> 0.99 < r < 1.01</p> <p> r ≤ 0.99</p> <p><u>Comment:</u></p> <p>Using forest area in 2015 as denominator for estimating this indicator ensures that the time series of percentages reflect real changes in the forest area within legally established protected areas and is not affected by changes (losses or gains) in total forest area. </p> <p><strong>Sub-indicator 4 – Proportion of forest area under a long-term management plan.</strong></p> <p><u>Unit</u>: Percent</p> <p><u>Reference year:</u> Latest reporting year</p> <p><u>Method of estimation:</u> </p> <p><math xmlns="http://www.w3.org/1998/Math/MathML"> <mi>r</mi> <mo>=</mo> <mi>&nbsp;</mi> <mfrac> <mrow> <msub> <mrow> <mi>A</mi> <mi>F</mi> <mi>M</mi> <mi>P</mi> </mrow> <mrow> <mfenced open="[" close="]" separators="|"> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> <mi>e</mi> <mi>r</mi> <mi>e</mi> <mi>n</mi> <mi>c</mi> <mi>e</mi> <mi>&nbsp;</mi> <mi>y</mi> <mi>e</mi> <mi>a</mi> <mi>r</mi> </mrow> </mfenced> </mrow> </msub> </mrow> <mrow> <msub> <mrow> <mi>A</mi> <mi>F</mi> </mrow> <mrow> <mn>2015</mn> </mrow> </msub> </mrow> </mfrac> <mo>×</mo> <mn>100</mn> </math></p> <p>Where:</p> <p><em>AFMP</em> = Forest area under a long-term management plan</p> <p><em>AF</em> = Total forest area</p> <p><u>Translation to dashboard/traffic light: </u>The indicator value for latest reporting year is compared with the indicator value reported for 2010.</p> <p>The ratio (r) between the current indicator value and the value reported for 2010 is calculated; r>1 means an increase in areas under management plan, r<1 means a decrease while 1 indicates no change. A narrow interval for r has been established to indicate a stable condition, and traffic-light colors are assigned as follows:</p> <p> r ≥ 1.01 </p> <p> 0.99 < r < 1.01</p> <p> r ≤ 0.99</p> <p><u>Comment:</u></p> <p>Using forest area in 2015 as denominator for estimating this indicator ensures that the time series of percentages reflect real changes in the forest area under management plan and is not affected by changes (losses or gains) in total forest area. </p> <p><strong>Sub-indicator 5 – Forest area under an independently verified forest management certification scheme.</strong></p> <p><u>Unit</u>: Thousand hectares</p> <p><u>Reference year:</u> Latest reporting year (as of June 30)</p> <p><u>Method of estimation:</u> Data is collected directly from the databases of each certification scheme and provided to countries for validation.</p> <p><u>Translation to dashboard/traffic light: </u>The indicator value for latest reporting year is compared with the indicator value for previous reporting year for assessment of continuity of progress since last report.</p> <p>The ratio (r) between the current indicator value and the previously reported value is calculated; r>1 means an increase in areas under an independent forest management certification scheme, r<1 means a decrease while 1 indicates no change. A small interval for r has been established to indicate a stable condition, and traffic-light colors are assigned as follows:</p> <p> r ≥ 1.01 </p> <p> 0.99 < r < 1.01</p> <p> r ≤ 0.99</p> <p><u>Comments:</u></p> <p>Using June 30 as the date for reporting, allows for the certification bodies to have their databases updated so they can provide information to FAO by end of the year, and then be included in the annual reporting to SDG in the beginning of the following year.</p><div class="footnotes"><div><sup class="footnote-number" id="footnote-2">1</sup><p> If forest area change rate is negative (= forest loss) then: annual forest area loss rate = <strong>-</strong> (annual forest area change rate) <a href="#footnote-ref-2">↑</a></p></div></div> |
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<p>The definition of SFM by the UN General Assembly contains several key aspects, notably that sustainable forest management is a concept which varies over time and between countries, whose circumstances – ecological, social and economic – vary widely, but that it should always address a wide range of forest values, including economic, social and environmental values, and take intergenerational equity into account. </p>
<p>Clearly a simple measure of forest area is insufficient to monitor sustainable forest management as a whole. The significance of the five sub-indicators can be briefly explained as follows: </p> <ol> <li>Trends in forest area are crucial for monitoring SFM. The first sub-indicator focuses on both the direction of change (whether there is a loss or gain in forest area) and how the change rate varies over time; the latter is important to capture progress among countries that are losing forest area but have managed to reduce the rate of annual forest area loss. </li> <li>Changes in the above-ground biomass stock in forest indicate the balance between gains in biomass stock due to forest growth and losses due to wood removals, natural losses, fire, wind, pests and diseases. At country level and over a longer period, sustainable forest management would imply a stable or increasing biomass stock per hectare, while a long-term reduction of biomass stock per hectare would imply either unsustainable management of the forests and degradation or unexpected major losses due to fire, wind, pests or diseases. </li> <li>The change in forest area within legally protected areas is a proxy for trends in conservation of forest biodiversity as well as cultural and spiritual values of forests and thus a clear indication of the political will to protect and conserve forests. This indicator is related to the CBD Aichi Target 11 which calls for each country to conserve at least 17 per cent of terrestrial and inland water areas. </li> <li>The fourth sub-indicator looks at the forest area that is under a long-term forest management plan. The existence of a documented forest management plan is the basis for long term and sustainable management of the forest resources for a variety of management objectives such as for wood and non-wood forest products, protection of soil and water, biodiversity conservation, social and cultural use, and a combination of two or several of these. An increasing area under forest management plan is therefore an indicator of progress towards sustainable forest management. </li> <li>The fifth sub-indicator is the forest area that is certified by an independently verified forest management certification scheme. Such certification schemes apply standards that generally are higher than those established by the countries’ own normative frameworks, and compliance is verified by an independent and accredited certifier. An increase in certified forest area therefore provides an additional indication of progress towards sustainable forest management. It should however be noted that there are significant areas of sustainably managed forest which are not certified, either because their owners have chosen not to seek certification (which is voluntary and market-based) or because no credible or affordable certification scheme is in place for that area. </li> </ol>
<h2> Обоснование: </h2>
<p>Определение рационального управления лесом (РУЛ), данное Генеральной Ассамблеей ООН, содержит несколько ключевых аспектов, в частности следует отметить, что рациональное управление лесом - это концепция, которая меняется как во временном, так и в межстрановом аспектах, чьи –экологические, социальные и экономические – обстоятельства варьируются в широких пределах, но при этом всегда следует учитывать широкий спектр ценностей леса , включая экономические, социальные и экологические ценности, и принимать во внимание справедливое распределение ресурсов между поколениями.</p> <p> Очевидно, что простого измерения площади лесов недостаточно для мониторинга рационального лесопользования в целом. Значение пяти субпоказателей можно кратко пояснить следующим образом: </p> <ol> <li> Тенденции изменения площади лесов имеют решающее значение для мониторинга РУЛ. Первый субпоказатель фокусируется как на направлении изменения (имеет ли место убыль или прирост площади лесов), так и на том, как скорость изменения варьируется с течением времени; последнее важно для отражения прогресса среди стран, теряющих площади лесов, но которые сумели снизить темпы ежегодной убыли лесных угодий. </li> <li> Изменения в запасах надземной биомассы в лесу указывают на баланс между приростом запасов биомассы в результате роста леса и потерями из-за вырубки древесины, естественной убыли, пожара, ветра, вредителей и болезней. На страновом уровне и в течение более длительного периода устойчивое лесопользование будет означать стабильный или увеличивающийся запас биомассы в расчете на гектар, в то время как долгосрочное сокращение запасов биомассы в расчете на гектар будет означать либо нерациональное управление лесами и деградацию, либо неожиданные крупные потери из-за огня, ветра, вредителей или болезней.</li> <li> Изменение площади лесов в пределах охраняемых законом территорий является косвенным показателем тенденций в сохранении биоразнообразия лесов, а также культурных и духовных ценностей лесов и, таким образом, явным свидетельством политической воли к защите и сохранению лесов. Этот показатель связан с целевой задачей 11 Конвенции по сохранению биологического разнообразия, принятой в Айти, которая призывает все страны сохранять не менее 17 процентов наземных и внутренних водных территорий.</li> <li>В четвертом субпоказателе рассматривается площадь лесов, на которую распространяется долгосрочный план управления лесами. Наличие задокументированного плана управления лесами является основой для долгосрочного и устойчивого управления лесными ресурсами для различных целей использования, таких как потребление древесных и недревесных лесных продуктов, защита почвы и воды, сохранение биоразнообразия, социальное и культурное использование, а также сочетание двух или нескольких из них. Таким образом, увеличение площадей в соответствии с планом управления лесами является показателем прогресса в направлении рационального использования лесов.</li> <li> Пятый субпоказатель - это площадь лесов, сертифицированных по схеме сертификации лесов, прошедшей независимую оценку. В таких схемах сертификации применяются стандарты, которые, как правило, выше, чем стандарты, установленные в рамках собственной нормативной страновой базы, и их соответствие требованиям проверяется независимым аккредитованным органом по сертификации. Таким образом, увеличение площади сертифицированных лесов является дополнительным показателем прогресса в обеспечении рационального лесопользования. Однако следует отметить, что существуют значительные площади рационально используемых лесов, которые не сертифицированы либо потому, что их владельцы предпочли не проходить сертификацию (которая является добровольной и осуществляется на рыночных условиях), либо потому, что не существует надежной или доступной схемы сертификации для этой области.</li> </ol> |
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<p><strong>Definition: </strong></p>
<p>“Sustainable forest management” (SFM) is a central concept for Goal 15 and target 15.1 as well as for target 15.2. It has been formally defined, by the UN General Assembly, as follows: </p> <p><em>[a] dynamic and evolving concept [that] aims to maintain and enhance the economic, social and environmental values of all types of forests, for the benefit of present and future generations</em>” (Resolution A/RES/62/98) </p> <p>The indicator is composed of five sub-indicators that measure progress towards all dimensions of sustainable forest management. The environmental values of forests are covered by three sub-indicators focused on the extension of forest area, biomass within the forest area and protection and maintenance of biological diversity, and of natural and associated cultural resources. Social and economic values of forests are reconciled with environmental values through sustainable management plans. The sub-indicator provides further qualification to the management of forest areas, by assessing areas which are independently verified for compliance with a set of national or international standards. </p> <p>The sub-indicators are: </p> <ol> <li>Annual forest area change rate </li> <li>Above-ground biomass in forest </li> <li>Proportion of forest area within legally established protected areas </li> <li>Proportion of forest area under a long-term management plan </li> <li>Forest area under an independently verified forest management certification scheme </li> </ol> <p>A dashboard is used to assess progress related to the five sub-indicators. The adoption of the dashboard approach aims at ensuring consideration of all dimensions of sustainable forest management and provides for clear view of areas where progress has been achieved. </p> <p><strong>Concepts: </strong></p> <p>See Annex 1 with Terms and Definitions. </p>
<h1> Понятия и определения </h1>
<h2> Определение: </h2> <p> “Неистощительное ведение лесного хозяйства (НВЛХ) является центральной концепцией цели 15 и задачи 15.1, а также задачи 15.2. Генеральная Ассамблея ООН официально определила его следующим образом: </p> <p> <em> [a] динамичная и эволюционирующая концепция, (которая) нацелена на сохранение и повышение экономической, социальной и экологической ценности всех видов лесов на благо нынешнего и будущих поколений. </em>(Резолюция A/RES/62/98)</p> <p> Показатель состоит из пяти субпоказателей, которые отражают прогресс во всех аспектах неистощительного ведение лесного хозяйства. Экологические ценности лесов охватываются тремя субпоказателями, сфокусированными на увеличении площади лесов, биомассы в пределах лесной площади и защите и поддержании биологического разнообразия, а также природных и связанных культурных ресурсов. Социальные и экономические ценности лесов согласовываются с экологическими ценностями посредством планов рационального использования. Субпоказатель накладывает дополнительные квалификационные требования к использованию лесных территорий путем оценки территорий, которые проходят независимую проверку на соответствие ряду национальных или международных стандартов. </p> <p> Субпоказатели: </p> <ol> <li> Темп годового чистого изменения площади лесов </li> <li>Запасы наземной лесной биомассы </li> <li>Доля лесных площадей, расположенных в пределах установленных законом охраняемых территорий </li> <li>Доля лесных площадей, имеющих долгосрочный план управления лесами </li> <li>Площадь лесов, на которых действует система сертификации использования лесов, прошедшая независимую проверку </li> </ol> <p> Информационная панель используется для оценки прогресса, связанного с пятью субпоказателями. Принятие подхода с использованием информационной панели направлено на обеспечение учета всех аспектов рационального лесопользования и дает четкое представление об областях, в которых был достигнут прогресс. </p> <p> <strong> Понятия: </strong> </p> <p> См. Приложение 1 Обозначения и Определения. </p> |
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<p> Последнее обновление: март 2020 года </p>
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<p>Annual forest area change rate (%) (AG_LND_FRSTCHG)</p>
<p>Above-ground biomass in forest (tonnes per hectare) (AG_LND_FRSTBIOPHA)</p> <p>Proportion of forest area within legally established protected areas (%) (AG_LND_FRSTPRCT)</p> <p>Proportion of forest area with a long-term management plan (%) (AG_LND_FRSTMGT)</p> <p>Forest area under an independently verified forest management certification scheme (thousands of hectares) (AG_LND_FRSTCERT)</p> |
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<p>Indicator 15.2.1: Progress towards sustainable forest management</p>
<p>Показатель 15.2.1: Прогресс в переходе на неистощительное ведение лесного хозяйства</p>
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