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Article

Reliability and Integrity of Forest Sector Statistics—A Major Constraint to Effective Forest Policy in Russia

by 1,2,*, 1,2 and 3,4
1
School of Economics, Siberian Federal University, 660041 Krasnoyarsk, Russia
2
Institute of Economics and Industrial Engineering, Siberian Branch, Russian Academy of Sciences, 630090 Novosibirsk, Russia
3
School of Ecology and Geography, Siberian Federal University, 660041 Krasnoyarsk, Russia
4
V.N. Sukachev Institute of Forest, Siberian Branch, Russian Academy of Sciences, 660036 Krasnoyarsk, Russia
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(1), 86; https://doi.org/10.3390/su13010086
Received: 24 November 2020 / Revised: 19 December 2020 / Accepted: 21 December 2020 / Published: 23 December 2020
(This article belongs to the Section Economic and Business Aspects of Sustainability)

Abstract

:
Russia owns one-fifth of the world’s forest-covered area but has never been the leader of the global forest sector nor in gross output or relative productivity. The issues of the Russian forest sector have attracted research attention, but for many topics, this is still a green field on the map of sectoral studies. We developed a novel approach to understand the primary causes of the inefficiency of the Russian forest policy through the qualitative assessment of completeness and reliability of forest sector-related data. The main output of this paper is a thorough overview of the available sources of data with an assessment of their quality, completeness and reliability. We show that the Russian official forest sector statistics provide only basic indicators for very short periods with few observations being incomplete and inconsistent. Besides a critical analysis of the official statistics, we also discover some known, but still underemployed, resources of information on the Russian forest sector: textual information of official public bodies and companies, accounting records, remote-sensing data, etc. Finally, we discuss the possible ways to improve the data procurement of the forest sector in Russia to support future decision-making. We are convinced that a prerequisite for the implementation of effective forest policy in Russia is a significant expansion and improvement of the volume and quality of statistics on the dynamics of Russian forests and forest economy. Integration of existing and new data sources is necessary to achieve synergistic effects, both in terms of deepening the understanding of key business processes in the industry and in the sense of solving strategic tasks of its development.

1. Introduction

Digitalization of the modern economy requires new approaches to dealing with information, even in traditional sectors, such as forestry and forest industry [1]. The amount and quality of available information on every small detail of sector development become a competitiveness factor if there is a goal to establish and follow sustainable practices.
Nonetheless, this problem is not in the primary academic or industrial focus. The lack of forest sector statistics seems to be an important problem, not only for an understanding of specific country-level processes but also for international comparisons and policy implications. For instance, the widely used data sets from FAOSTAT, the UN Food and Agricultural Organization database, were proven to contain systematic errors in records on production, imports and exports of some forest products in many countries [1,2]. Some observations for basic indicators are missing, even for such developed countries, such as Canada and Japan [3]. The lack of robust data on different ecological and economic aspects of forest dynamics is of crucial importance for policymaking that aims to achieve the goals of sustainable development, in terms of the United Nations [4]. The interlinkages between data governance and forest governance need a further detailed investigation as well [5].
The quality and diversity of the Russian official statistical information are also matters of concern for scholars due to a set of widely known issues described in a qualitative way [6,7,8]. It is also worth mentioning that due to few similar institutional features, the Russian statistical system and its shortcomings resemble those of China [9,10,11,12], but still are not covered by the quantitative macro-analysis and specific sector-level studies.
Russia owns 1/5 of the world’s forest-covered area but has never been the leader of the global forest sector nor in gross output or relative productivity. According to the recent data from FAO, Russia accounted only for 6% of wood removals in 2018 (218.4 mln cub. m), behind the USA (11%), India (9%), China (9%) and Brazil (7%) [13]. According to the official data, most parts of Russian forests are managed (88.8% in 2018) (NIR 2019), however, Russia uses only 30% of its allowable cut leaving a gross amount of forest resources out of business [14,15,16].
The reasons for the current state of the Russian forest sector are discussed in the literature [17]. First of all, the Russian government is poorly involved in sector regulation [18,19,20,21]. In recent decades, there were several changes of sector legislation, but they never brought positive institutional development, neither implementation of sustainable forest management practices. Most of Russian commercial forests are governed under the predatory regime where clear cuts are not balanced with appropriate reforestation and afforestation activities [18,19].
The Russian forest sector is unprofitable, even for the government. As of 2016, the Russian federal budget has spent RUB 59.5 B to maintain forest management raising only RUB 29.7 B as stumpage fees—the net loss was 50% [14]. The analysis of dynamics of this ratio shows that this tendency holds for a very long period indicating that the situation is acceptable for policymakers.
Most researchers state that the main institutional source of the problems described above is the pronounced path-dependence derived from the resource-intensive and environmentally indifferent forest policy of the Soviet period [22]. These adverse initial conditions were enhanced by the fast centralization of the forest management in the 2000s—a reform that has produced more negative than positive consequences [17,23].
Beyond the in-country forest sector agenda, there is also a growing interest in the potential impact of climate change on forestry [24,25,26,27]. It could only be satisfied if the necessary amount of data is available for massive calculations based on the state-of-the-art models.
An important reason of forest sector crisis in Russia is the deterioration of the forest science system after the collapse of the Soviet Union in the 1990s when a few strong academic and industry research institutes ceased to exist or dramatically lost their potential. The main consequence is the lack of data and research results that are needed to understand the dynamics and future of forest economics in Russia. Many important topics that could give important knowledge for policymaking are still undercovered by appropriate research. E.g., despite there being very high-quality empirical studies on typology of the principal stakeholders in many European post-Soviet countries, the corresponding works on Russia are completely missing.
In our study, we introduce a novel approach to understand the in-depth nature and genesis of factors limiting the development of the Russian forest sector. The key assumption of our study is that one of the main reasons for the crisis in the Russian forest sector is the lack of adequate and high-quality statistics and studies on different aspects of its development.
This paper aims to give a systematic overview of the key data sources on the Russian forest sector and describe the openness and completeness of statistics on different topics that are important for policymaking aimed at sustainable forest management goals.

2. Materials and Methods

We suggest the use of the following classification for main sources of open data on forest management and forest economics in Russia:
  • Official data from Rosstat, Rosleskhoz and the appropriate public bodies (ministries and departments);
  • Press releases issued by Rosleskhoz, its regional offices and forest companies;
  • Industry magazines (Lesprominform [28], LPK Sibiri and others);
  • Accounting records available through commercial services (SPARK-Interfax [29], Kontur.Focus [30], etc.);
  • Open satellite images and related remote sensing data.
It should be stressed that only digitalized data are in the focus of our research. This assumption constricts the period of observations to the years after the collapse of the Soviet Union, as due to some unobvious reasons, the “old” statistics (i.e., the statistics of the Soviet period, between 1922 and 1991) are absent in the open official publications. If needed, such information could be retrieved from then-dated statistical books and reports (e.g., [31]).
Since 2011, Rosstat, the official public statistics body of the Russian Federation, has launched the Unified Interagency Information and Statistical System (EMISS) project [32] that integrates the statistics provided by all the federal public authorities. The purpose of this project is the gradual integration of all official state statistics on a single platform that provides a unified interface for data access. We employ EMISS as the main interface to Rosstat data, as it contains more up-to-date statistics and thus provides more recent data rather than conventional Rosstat data books. The use of EMISS is similar to any modern open database with a user-friendly web interface and advanced capabilities of full-text and structured search, spreadsheet-like view and multi-format output of the final sample of data.
A usual approach to work with these data is to search the needed statistical indicators by common keywords (such as forest or wood) or to filter the records by the corresponding public body (e.g., (Rosleskhoz)). For our research, we employed another method making an indicator-to-indicator selection from the whole database (Section 3.1). At the first stage, we select all the indicators that are connected with the forest sector accomplishing the following routines:
  • Omitting the indicator that contains less than three observation periods, most of them were included in the official statistical observation only to accomplish some tactical task, but not to establish a new indicator that could produce a reliable time series.
  • Aggregation by the similar indicator goal, as there are multiple cases of slight changes of indicator titles over the course of observation.
  • Minor groupings of related indicators could also be done, for the sake of simplicity and easy representation.
  • Omitting the indicators that do not interfere with main forest-related activities (such as Turnover of public food, i.e., the total turnover of canteen services for employees of forest industries).
  • Omitting the indicators that could be calculated using the other ones presented in the dataset, e.g., if there are data on total forested area, and the total area is also known, the indicator for relative land area forestation is redundant and needs to be omitted.
At the second stage, the indicators are clustered by the following topics: Companies: demography, Companies: business, Labor, Products and Prices, Lands and Growing Stock, Reforestation, Disturbances, Forest Protection, Forest Management.
The indicators may be observed for different sections: federal (or national), federal districts and regions. We do not account for sub-national, federal districts data, due to the following reasons: (a) these bodies are mostly political and almost do not interfere with economic activity, (b) the composition of these bodies is unstable and has changed several times during previous decades, (c) it is not evident how to use the implications from the analysis of federal districts data to the solution of real problems, as these bodies do not have any decision-making authorities.
Study and analysis of other data sources were made using classical descriptive methods (Section 3.2, Section 3.3, Section 3.4 and Section 3.5).

3. Results

3.1. Official Public Statistics

The EMISS database contains 7010 different indicators. The in-depth analysis was aimed at indicator selection using the approach described in the previous section, which led to 1044 indicators after the first stage. The table with raw data contains 18,477 filled cells with the following columns: title, units of measure, full description, observation period and section, link, and official service, which is responsible for this indicator.
After the second stage, only a few dozen aggregated indicators remain in sight. For convenience, we split the list of reviewed statistical indicators into two separate tables: one for economic data (Table 1) and the second for the indicators on forest management (Table 2).
It is evident that the Russian official forest sector statistics provide only basic indicators, and time series are still very short. For the most part of indicators, only 10–15 years of observations are available, which is usually a very short period for time series modelling.
During the last years, monitoring of a new set of indicators has started, but it is not obvious if they will be observed in mid- or long-run perspective, e.g., there are detailed data on different topics in forest health protection (such as the estimates of economic losses due to forest pest outbreaks), but only the values for the last few years are available, so it is not possible to use it as a calculation-ready time series. Some indicators are “orphaned”, i.e., there are only one or two observations in previous years, but it seems that the indicator is not maintained anymore.
Random sample check and the accumulated previous experience show that for a sufficient share of indicators there is also a problem of inconsistency and incompleteness of the data. The total check and quantitative assessment of the share of missed observations could become a subject of further research, as this task is quite large (e.g., the full stack of data for only one indicator may exceed several thousands of observations). Observations for some years are missing. In other cases, there is obvious incoherence, e.g., the indicator may double for only one year, staying almost stable during the other periods. In both described cases, no explanations are provided, so it is reasonable to question at least part of the observations in such indicators.
It is worth emphasizing that we did not find the direct use of these new indicators in the most important current publicly available policymaking documents of the forest authorities.
Despite the mentioned shortcomings, it is fair to emphasize that the total statistics revealed during this study cover a few topics that are not usually discussed in the academic literature. Thus, there is potential to sufficiently develop the research agenda to these topics. First, that is true for the study of company–scale dynamics of the forest sector. The worldwide interest in this topic is growing [33,34,35] but has not yet been touched upon in Russian studies. Second, the problem of forest pest management is also of great practical importance, especially for Siberian forests, but is very moderately covered in the literature.

3.2. Data on International Trade

Data on international trade of forest products are available through two major sources: FAOSTAT resource [36] and database of the Federal Customs Service of Russia (FTS) [37].
FAOSTAT is the commonly used international freely available database, which provides, inter alia, the aggregated macro-level data on trade flows of nine types of forest products between 245 world countries since 1997. The primary source of FAO data is always the authorized national services, so, usually, the FAOSTAT data may also be retrieved from some local and native-speaking resource. However, the advantage of accessing the data through FAOSTAT is the user-friendly interface and the possibility to track all the necessary trade flows in one place, which is of crucial importance when making the cross-country comparisons.
The high level of data aggregation of these data makes them almost useless when dealing with specific research issues focused on a single country. In such cases, the data from local sources, such as FTS, are much more appropriate. The FTS database includes a large set of export and import monthly statistics, accounted for both in physical and monetary terms. In addition to national data, there are also statistics on foreign trade of Russian regions. All data are classified with the Harmonized Commodity Description and Coding System and are available for up to ten-digit codes. In comparison, the UN Comtrade database [38] is limited to a six-digit code. Using disaggregated statistics can help to examine the trade patterns more thoroughly with a focus on specific commodities. For example, using eight-digit codes is necessary to clear the data on such wooden products as furniture from the presence of other materials such as metal or plastic. This approach was developed and applied to assess the competitiveness level of Russian forest industry products [15].

3.3. Textual Information

An important, but very underestimated, source of data on forest economics is the textual information that may be distilled from the official press releases of both public bodies and companies, as well as industry magazines, social media and public forums.
Since the 1990s, industry magazines became a very influential communication platform for industry insiders, especially on the business side. The most important title, Lesprominform, publishes full issues in PDF format after a short embargo period (2–3 months). The website of the magazine (they are also presented on Facebook, VK, Instagram social media) is a major resource of the Russian forest sector news and updates. In addition to interviews and companies’ press releases, they also publish some pieces of the latest statistics on different aspects of forest sector activity.
There is no evidence of complex analysis of the above-listed source, but this idea has good potential for retrieving some new and independent data of the real situation inside the industry.

3.4. Accounting Records

In Russia, the Federal Tax Service (FNS) provides only general information on registered companies, but not the accounting records. These data may be accessed through different web services for a reasonable price (Table 3).
The main advantage of this data source is that it opens new directions of data analysis of forest sector companies, as almost all the services provide different extra information datasets in addition to the usual accounting records. Most of them summarize the list of arbitration cases, government contracts, media references and other information that could be useful for further studies.
All these systems are primarily developed for counterparty screening, so there are some limitations for scientific use. The most important limitation is that the access API is too expensive (e.g., for the cheapest service, SBIS, it will cost more than RUR 1 M USD 12,600 per 100,000 companies). As a manual export of data into a spreadsheet format is also not provided, the only reasonable way to sample companies is to manually save the necessary information from a company account. This is a major obstacle to making big samples ( N > 100 ).

3.5. Remote Sensing Data

The openness of the global satellite image data allows one to cross-check the official government data. As this is a very resource-intensive task, the corresponding studies are still sparse but tend to grow up in subsequent years [39,40,41].
Despite the growing tendency of remote-sensing-driven ecological forest studies in Russia [42,43,44,45,46], the economic aspects are almost not covered within this topic.
Nevertheless, it is the remote sensing data that can provide an invaluable source of primary observations for economic research. Such an interlinkage of methods widely used in forest management and the aims of economic studies are needed to understand the real situation with forest use. Special attention to the problems of shadow cut is needed, as this problem is of great importance in many remote theatres of logging activities.
A valuable source of preprocessed satellite data on tree cover dynamics, since 2001, has been the Global Forest Watch project [47]. Using these data will require time-consuming work, as they are not available in raw formats. Collection and quantification of these data linked to regional- and firm-scale data may create strong perspectives of research with policymaking outcomes.

4. Conclusions

We develop a novel approach to understanding the primary causes of inefficiency of the Russian forest policy through the qualitative assessment of completeness and reliability of forest sector-related data.
Our analysis showed that the Russian official forest sector statistics provide only basic indicators for very short time periods. Many indicators contain inconsistent and incomplete data sets. However, even the existing data are not fully employed nor for academic research, neither for policymaking. An important finding is that the indicators newly registered by the official statistics in recent years are not used for real policymaking workflows. The statistics of the Soviet period (pre-1991) are not digitalized but could be accessed in statistical books and reports of then-existing industry research institutes. The data are much poorer in terms of a variability of indicators but seem to be more reliable.
A new data set could be compiled using the classification of official statistical indicators that we suggested in this paper. It aggregates all the data that one could extract from the official statistics. Most of these data were highly likely unused to analyze the practical issues of the forest sector in Russia, so it becomes possible to acquire a piece of new knowledge on different aspects of sector development.
A very important source of forest sector data is international trade statistics. The Russian Federal Customs Service (FTS) provides a large set of data that could be used to track trade flows of forest products with a high detail level (six-digit codes according to Harmonized Commodity Description and Coding System).
Textual information distilled from official press releases and other open web-sources (industry magazines, social media, public forums) is a data source with underestimated potential. As the quantity of such information is growing up, the necessity of its employment for research will become more and more relevant.
Accounting records are widely used in other economic research but were never employed to study some aspects of the Russian forest sector economic problems. These data are not available for free, but the price is feasible.
Remote sensing data should also be widely employed for tracking the natural and anthropogenic forest dynamics. There are numerous sources of freely available data, but its processing requires time-intensive work.
We argue that a prerequisite for the implementation of effective forest policy in Russia is a significant expansion and improvement of the volume and quality of statistics on the dynamics of Russian forests and forest economy. It seems to be quite a difficult task, which should involve the representatives of both academic and business communities. The practical use of the data sources analyzed above can contribute to the first steps towards this task.

Author Contributions

Conceptualization, A.I.P.; methodology, A.I.P. and R.V.G.; formal analysis, A.I.P.; data curation, R.V.G.; writing (original draft preparation), A.I.P.; writing (review and editing), R.V.G., E.A.V.; visualization, A.I.P.; supervision, A.I.P.; project administration, A.I.P.; funding acquisition, E.A.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Science and Higher Education of the Russian Federation within the framework of grant for large scientific projects in priority directions of scientific and technological development no. 075-15-2020-804/13.1902.21.0016 dated 02.10.2020 entitled “Socio-economic development of Asian Russia based on the synergy of transport accessibility, system knowledge about natural resource potential, expanding space of inter-regional interactions”.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: https://www.fedstat.ru/.

Acknowledgments

The authors express their deep gratitude to the three anonymous peer reviewers; their extensive comments were valuable to sufficiently improve the original manuscript.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
APIApplication Programming Interface
EMISSUnified Interagency Information and Statistical System (State Statistics (from Rosstat))
FAOFood and Agricultural Organization (United Nations)
FAOSTATFood and Agriculture Organization Corporate Statistical Database
FNSFederal Tax Service of Russia
FTSFederal Customs Service of Russia
RosstatFederal State Statistics Service of Russia
RosleskhozFederal Agency for Forestry of Russia

References

  1. Kallio, A.M.I.; Solberg, B. On the Reliability of International Forest Sector Statistics: Problems and Needs for Improvements. Forests 2018, 9, 407. [Google Scholar] [CrossRef][Green Version]
  2. Buongiorno, J. On the accuracy of international forest product statistics. Forestry 2018, 91, 541–551. [Google Scholar] [CrossRef]
  3. Pyzhev, A.I. Impact of the Ownership Regime on Forest Use Efficiency: Cross-Country Analysis. J. Inst. Stud. 2019, 11, 182–193. [Google Scholar] [CrossRef]
  4. Ibáñez, I.; Acharya, K.; Juno, E.; Karounos, C.; Lee, B.R.; McCollum, C.; Schaffer-Morrison, S.; Tourville, J. Forest resilience under global environmental change: Do we have the information we need? A systematic review. PLoS ONE 2019, 14, e0222207. [Google Scholar] [CrossRef]
  5. Rantala, S.; Swallow, B.; Paloniemi, R.; Raitanen, E. Governance of forests and governance of forest information: Interlinkages in the age of open and digital data. For. Policy Econ. 2020, 113, 102123. [Google Scholar] [CrossRef]
  6. Bessonov, V.A. What Will the modern Russian Statistics have in Store for history? Vopr. Ekon. 2015, 1, 125–146. [Google Scholar] [CrossRef]
  7. Smirnov, S. Economic Fluctuations in Russia (from the late 1920s to 2015). Russ. J. Econ. 2015, 1, 130–153. [Google Scholar] [CrossRef][Green Version]
  8. Shirov, A.A. Statistics for the Benefit of Economics and Society. Stud. Russ. Econ. Dev. 2020, 31, 3–6. [Google Scholar] [CrossRef]
  9. Holz, C.A. “Fast, Clear and Accurate”: How Reliable Are Chinese Output and Economic Growth Statistics? China Q. 2003, 173, 122–163. [Google Scholar] [CrossRef][Green Version]
  10. Holz, C.A. The quality of China’s GDP statistics. China Econ. Rev. 2014, 30, 309–338. [Google Scholar] [CrossRef]
  11. Rawski, T.G. What is happening to China’s GDP statistics? China Econ. Rev. 2001, 12, 347–354. [Google Scholar] [CrossRef]
  12. Liu, G.; Wang, X.; Baiocchi, G.; Casazza, M.; Meng, F.; Cai, Y.; Hao, Y.; Wu, F.; Yang, Z. On the accuracy of official Chinese crop production data: Evidence from biophysical indexes of net primary production. Proc. Natl. Acad. Sci. USA 2020, 117, 25434–25444. [Google Scholar] [CrossRef] [PubMed]
  13. FAO. Global Forest Resources Assessment 2020: Main Report; FAO: Rome, Italy, 2020. [Google Scholar] [CrossRef]
  14. Petrov, V.; Katkova, T.; Karvinen, S. Comparative Analysis of Forestry Economic Indicators of Russia and Finland. HSE Econ. J. 2018, 22, 294–319. [Google Scholar] [CrossRef]
  15. Gordeev, R. Comparative advantages of Russian forest products on the global market. For. Policy Econ. 2020, 119, 102286. [Google Scholar] [CrossRef]
  16. Russian Federation. 2020 National Inventory Report (NIR). Available online: https://unfccc.int/sites/default/files/resource/rus-2020-nir-15apr20.zip (accessed on 5 November 2020).
  17. Zubkov, V. The Role of the State in the Development of Russia Forestry Complex. Vopr. Ekon. 2010, 6, 118–126. [Google Scholar] [CrossRef]
  18. Olsson, M.O. Systemic Interventions to Promote Institutional Change in the Russian Forest Sector. Rev. Policy Res. 2006, 23, 505–530. [Google Scholar] [CrossRef]
  19. Antonova, N.E.; Volkov, L.V. Prospects of transformation of the complex on use of biological resources of the Pacific Russia. Econ. Reg. 2012, 3, 168–178. [Google Scholar] [CrossRef]
  20. Antonova, N.E.; Lomakina, N.V. Institutional Innovations for the Development of the East of Russia: Effects of Implementation in the Resource Region. J. Sib. Fed. Univ. Humanit. Soc. Sci. 2020, 442–452. [Google Scholar] [CrossRef]
  21. Pyzhev, A.I. The Forest Complex of Russia Through the Mirror of the May 2018 Presidential Decree: Is it Worth Waiting for a Breakthrough? J. Econ. Regul. 2019, 10, 77–89. [Google Scholar] [CrossRef]
  22. Ulybina, O. Russian forests: The path of reform. For. Policy Econ. 2014, 38, 143–150. [Google Scholar] [CrossRef]
  23. Torniainen, T.J.; Saastamoinen, O.J.; Petrov, A.P. Russian forest policy in the turmoil of the changing balance of power. For. Policy Econ. 2006, 9, 403–416. [Google Scholar] [CrossRef]
  24. Lutz, D.A.; Shugart, H.H.; White, M.A. Sensitivity of Russian forest timber harvest and carbon storage to temperature increase. Forestry 2013, 86, 283–293. [Google Scholar] [CrossRef][Green Version]
  25. Torzhkov, I.O.; Kushnir, E.A.; Konstantinov, A.V.; Koroleva, T.S.; Efimov, S.V.; Shkolnik, I.M. Assessment of Future Climate Change Impacts on Forestry in Russia. Russ. Meteorol. Hydrol. 2019, 44, 180–186. [Google Scholar] [CrossRef]
  26. Pyzhev, A.I. Global climate change and logging volumes in Siberian regions from 1946 to 1992. Terra Econ. 2020, 18, 140–153. [Google Scholar] [CrossRef]
  27. Svetlov, N.M.; Siptits, S.O.; Romanenko, I.A.; Evdokimova, N.E. The Effect of Climate Change on the Location of Branches of Agriculture in Russia. Stud. Russ. Econ. Dev. 2019, 30, 406–418. [Google Scholar] [CrossRef]
  28. Lesprominform. Zhurnal Professionalov LPK (Forest Industry Information Magazine for the Professionals of Forest Industry). Available online: https://lesprominform.ru (accessed on 5 October 2020).
  29. SPARK-Interfax. Available online: https://spark-interfax.com (accessed on 5 October 2020).
  30. Kontur.Focus. Available online: https://focus.kontur.ru (accessed on 5 October 2020).
  31. Forest Management in the Russian Federation in 1946—1992; VNIITSlesresurs: Moscow, Russia, 1996.
  32. Rosstat. Edinaya mezhvedomstvennaya informatsionno-spravochnaya systema. Gosudarstvennaya statistika (Unified Interagency Information and Statistical System. State Statistics). Available online: https://www.fedstat.ru/ (accessed on 5 October 2020).
  33. Koellner, T.; Sell, J.; Navarro, G. Why and how much are firms willing to invest in ecosystem services from tropical forests? A comparison of international and Costa Rican firms. Ecol. Econ. 2010, 69, 2127–2139. [Google Scholar] [CrossRef]
  34. Frey, G.E.; Cubbage, F.W.; Holmes, T.P.; Reyes-Retana, G.; Davis, R.R.; Megevand, C.; Rodríguez-Paredes, D.; Kraus-Elsin, Y.; Hernández-Toro, B.; Chemor-Salas, D.N. Competitiveness, certification, and support of timber harvest by community forest enterprises in Mexico. For. Policy Econ. 2019, 107, 101923. [Google Scholar] [CrossRef]
  35. Caughron, A.; Legault, S.; Haut, C.; Houle, D.; Reynolds, T.W. A Changing Climate in the Maple Syrup Industry: Variation in Canadian and U.S.A. Producers’ Climate Risk Perceptions and Willingness to Adapt Across Scales of Production. Small-Scale For. 2020, 1–23. [Google Scholar] [CrossRef]
  36. FAO. FAOSTAT. Available online: http://www.fao.org/faostat/en/#home (accessed on 5 October 2020).
  37. Federal Customs Service. Available online: https://eng.customs.gov.ru (accessed on 5 October 2020).
  38. United Nations. UN Comtrade Database. Available online: https://comtrade.un.org/ (accessed on 5 October 2020).
  39. Shimizu, K.; Ota, T.; Mizoue, N. Accuracy Assessments of Local and Global Forest Change Data to Estimate Annual Disturbances in Temperate Forests. Remote Sens. 2020, 12, 2438. [Google Scholar] [CrossRef]
  40. Wulder, M.A.; Masek, J.G.; Cohen, W.B.; Loveland, T.R.; Woodcock, C.E. Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sens. Environ. 2012, 122, 2–10. [Google Scholar] [CrossRef]
  41. Wulder, M.A.; Loveland, T.R.; Roy, D.P.; Crawford, C.J.; Masek, J.G.; Woodcock, C.E.; Allen, R.G.; Anderson, M.C.; Belward, A.S.; Cohen, W.B.; et al. Current status of Landsat program, science, and applications. Remote Sens. Environ. 2019, 225, 127–147. [Google Scholar] [CrossRef]
  42. Kharuk, V.I.; Im, S.T.; Ranson, K.J.; Yagunov, M.N. Climate-Induced Northerly Expansion of Siberian Silkmoth Range. Forests 2017, 8, 301. [Google Scholar] [CrossRef][Green Version]
  43. Bergen, K.M.; Loboda, T.; Newell, J.P.; Kharuk, V.; Hitztaler, S.; Sun, G.; Johnson, T.; Hoffman-Hall, A.; Ouyang, W.; Park, K.; et al. Long-term trends in anthropogenic land use in Siberia and the Russian Far East: A case study synthesis from Landsat. Environ. Res. Lett. 2020, 15, 105007. [Google Scholar] [CrossRef]
  44. Shinkarenko, S.S.; Bartalev, S.A. NDVI seasonal dynamics of the North Caspian pasture landscapes from MODIS data. Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Iz Kosm. 2020, 17, 179–194. [Google Scholar] [CrossRef]
  45. Zharko, V.O.; Bartalev, S.A.; Sidorenkov, V.M. Forest growing stock volume estimation using optical remotese nsing over snow-covered ground: A case study for Sentinel-2 data and the Russian Southern Taiga region. Remote Sens. Lett. 2020, 11, 677–686. [Google Scholar] [CrossRef]
  46. Kharuk, V.I.; Im, S.T.; Soldatov, V.V. Siberian silkmoth outbreaks surpassed geoclimatic barrier in Siberian Mountains. J. Mt. Sci. 2020, 17, 1891–1900. [Google Scholar] [CrossRef]
  47. Global Forest Watch. Available online: https://www.globalforestwatch.org/map/ (accessed on 25 October 2020).
Table 1. Statistical indicators on the Russian forest economics available from Rosstat. * means that some observation periods are missing, or data frequency is uneven.
Table 1. Statistical indicators on the Russian forest economics available from Rosstat. * means that some observation periods are missing, or data frequency is uneven.
ClusterIndicatorObs. SectionFrequencyObs. Period
Companies: demographyNumber of registered companiesNationalHalf year2014–2020
No. of newly registered companiesNationalMonth2012–2020
No. of disbanded companiesNationalMonth2017–2020
No. of joint-stock companies owned by stateNationalHalf year2015–2020
No. of entrepreneurs (small-sized companies)NationalQuarter2013–2020
No. of large and mid-sized companiesNationalYear2005–2019
No. of companies with foreign (co-)ownershipRegionalYear2005–2015
Distribution of companies by the year of foundationRegional (–2016)Year2005–2019
No. of profitable companiesRegionalMonth2004–2020
Companies:
business
Goods and services outputRegionalMonth2005–2020
Turnover (excl. small-sized firms)RegionalYear2005–2019
Production and sales costsRegionalYear2005–2019
Depreciation of fixed assetsRegionalYear2005–2016
Accounts receivable and payable, profits (losses) before taxRegionalYear2004–2020
Capital investments (only large companies) *RegionalQuarter2005–2020
Value of foreign direct investmentsRegionalQuarter2004–2013
Rental payments in totalRegionalYear2005–2016
Rental payments for premises, land, machinery and equipment, finance leasingRegionalYear2014–2016
Water and land taxesRegionalYear2005–2016
Value of purchased water, gas, heat and electrical energyRegionalYear2005–2016
Taxes and fees paidRegionalYear2005–2016
Timber purchase and sale feesRegionalYear2005–2016
Value of research and development works made by subcontractorsRegionalYear2005–2016
LaborNo. of employeesRegionalYear2012–2019
Avg. monthly nominal salariesRegionalMonth2013–2020
No. of hired and dismissed employeesRegionalQuarter2014–2020
Products and PricesValue of goods and services outputRegionalMonth2005–2020
Production indicesRegionalMonth2000–2019
Quantity of goods and services outputNationalMonth2002–2018
Share of exports in total volume of trade turnoverRegionalQuarter2010–2020
Producer prices indicesRegionalMonth2000–2020
Value of imports and exports of selected forest productsRegionalMonth1994–2020
Source: developed by authors using the data from Rosstat using Unified Interagency Information and Statistical System (EMISS) platform. URL: https://fedstat.ru/. Note: statistical indicators on the Russian forest management available from Rosstat. The incl. mark points out that there are two or more separate corresponding indicators.
Table 2. Statistical indicators on the Russian forest management available from Rosstat. The incl. mark points out that there are two or more separate corresponding indicators.
Table 2. Statistical indicators on the Russian forest management available from Rosstat. The incl. mark points out that there are two or more separate corresponding indicators.
ClusterIndicatorObs. SectionFrequencyObs. Period
Lands and Growing StockLand area of the forest fund (incl. state- or municipal-owned, settlement, protected, industrial and reserve lands)RegionalYear1998–2019
Forest covered areaRegionalYear1998–2019
Total growing stockRegionalYear2009–2019
Area of forest under monitoring using remote sensingNationalYear2013–2017
ReforestationReforestation areaRegionalYear1992–2019
Reforestation and afforestation area on land contaminated by radiation (Chernobyl area)RegionalYear2000–2019
Afforestation due to water management activities (value and quantity)RegionalYear2012–2019
Expenditures on reforestationRegionalQuarter2009–2019
DisturbancesArea of forest losses due to natural disturbances (incl. young growth, coniferous forests)RegionalYear2000–2019
Area, volume and no. of forest firesRegionalYear1992–2012
Expenditures on forest fire fightingRegionalYear2000–2012
Area of fires on non-forested forest landsRegionalYear2000–2012
Pest outbreak area (incl. new)RegionalYear2000–2019
Reaction of pest outbreak (fade out naturally, destroyed by human activity, need further action)RegionalYear2000–2019
Forest ProtectionProtecting forests from pests using biological methodsRegionalYear1994–2016
Protecting forests from pests using chemical methodsRegionalYear2000–2016
Length of constructed firefighting roads and firebreaksRegionalYear2009–2018
Area of aerial and ground works on insect pest controlRegionalYear2009–2018
Area of sanitary cuttings and litter removalRegionalYear2009–2018
Expenditures on forest protection, forest fire preventionRegionalQuarter2009–2019
Forest ManagementHarvested wood volumeRegionalYear2009–2019
Government expenditures on forest managementRegionalQuarter2009–2019
Table 3. Main web services providing accounting records of Russian companies. The USD prices in parentheses are calculated for one-year basic subscription, as of 01.10.2020.
Table 3. Main web services providing accounting records of Russian companies. The USD prices in parentheses are calculated for one-year basic subscription, as of 01.10.2020.
ServicePriceComparative Characteristics
SPARK-InterfaxRUB 250,000 *
(USD 3174)
Best data diversity and most advanced interface
Kontur.FocusRUB 22,000
(USD 279)
Best price–quality ratio
Kommersant.KartotekaRUB 49,500
(USD 628)
The most inconvenient interface
SBISRUB 10,000
(USD 127)
The poorest extra data sources
Source: developed by authors. * as the official price list is not disclosed, the price estimate from an unreported source is used.
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Pyzhev, A.I.; Gordeev, R.V.; Vaganov, E.A. Reliability and Integrity of Forest Sector Statistics—A Major Constraint to Effective Forest Policy in Russia. Sustainability 2021, 13, 86. https://doi.org/10.3390/su13010086

AMA Style

Pyzhev AI, Gordeev RV, Vaganov EA. Reliability and Integrity of Forest Sector Statistics—A Major Constraint to Effective Forest Policy in Russia. Sustainability. 2021; 13(1):86. https://doi.org/10.3390/su13010086

Chicago/Turabian Style

Pyzhev, Anton I., Roman V. Gordeev, and Eugene A. Vaganov. 2021. "Reliability and Integrity of Forest Sector Statistics—A Major Constraint to Effective Forest Policy in Russia" Sustainability 13, no. 1: 86. https://doi.org/10.3390/su13010086

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