Can we Rely upon Fiscal Policy Estimates in Countries with Unreported Production of 15 Per Cent (or more) of GDP?

This paper analyzes the effects of fiscal policy in Italy by employing a database containing two statistical novelties: quarterly fiscal variables on accrual basis and a time series estimate of tax evasion for the period 1981:1-2006:4. Following Revenue Agency suggestions, we use in a VECM the time series of the concealed VAT base as a proxy for the size of “unreported production”, and define a regular GDP measure constructed as GDP net of government expenditure and evaded VAT base. The results reveal that we cannot rely upon the estimates of fiscal policy multipliers in countries with a sizeable unreported production unless the dynamics of the hidden and regular components of the GDP are disentangled. Changes in public spending and the tax rate generate a reallocation from underground to the regular economy which contributes to obscure the spending and tax effect on total GDP. In this setup the spending multiplier shows large long-run effects, considerably stronger than those registered in a model with no attention paid to unreported production. The drop in regular output, after an increase in the effective tax rate, tends to be considerable after one year, producing long-lasting effects and a significant increase in unreported production and tax evasion.fiscal policy, VECM, fiscal multipliers, unreported GDP, tax ratio, effective tax rate

Monitoring post-fire forest recovery using multi-temporal Digital Surface Models generated from different platforms

Wildfires can greatly affect forest dynamics. Given the alteration of fire regimes foreseen globally due to climate and land use changes, greater attention should be devoted to prevention and restoration activities. Concerning in particular post-fire restoration actions, it is fundamental, together with a better understanding of ecological processes resulting from the disturbance, to define techniques and protocols for long-term monitoring of burned areas. This paper presents the results of a study conducted within an area affected by a stand-replacing crown fire (Verrayes, Aosta (AO), Italy) in 2005, which is part of a long-term monitoring research on post-fire restoration dynamics. We performed a change detection analysis through a time sequence (2008-2015) of DSMs (Digital Surface Models) obtained from LiDAR (ALS - Airborne Laser Scanner) and digital images (UAV - Unmanned Aerial Vehicle flight) to test the ability of the systems (platform + sensor) to identify the ongoing processes. New technologies providing high-resolution information and new devices (i.e. UAV) able to acquire geographic data “on demand” demonstrated great potential for monitoring post disturbance recovery dynamics of vegetation

Post-Fire Management Impact on Natural Forest Regeneration through Altered Microsite Conditions

High severity stand-replacing wildfires can deeply affect forest ecosystems whose composition includes plant species lacking fire-related traits and specific adaptations. Land managers and policymakers need to be aware of the importance of properly managing these ecosystems, adopting post-disturbance interventions designed to reach management goals, and restoring the required ecosystem services. Recent research frequently found that post-fire salvage logging negatively affects natural regeneration dynamics, thereby altering successional pathways due to a detrimental interaction with the preceding disturbance. In this study, we compared the effects of salvage logging and other post-disturbance interventions (adopting different deadwood management strategies) to test their impact on microclimatic conditions, which potentially affect tree regeneration establishment and survival. After one of the largest and most severe wildfires in the Western Alps that affected stand-replacing behavior (100% tree mortality), a mountain forest dominated by Pinus sylvestris L., three post-fire interventions were adopted (SL-Salvage Logging, logging of all snags; CR-Cut and Release, cutting snags and releasing all deadwood on the ground; NI-No Intervention, all snags left standing). The differences among interventions concerning microclimatic conditions (albedo, surface roughness, solar radiation, soil moisture, soil temperature) were analyzed at different spatial scales (site, microsite). The management interventions influenced the presence and density of safe sites for regeneration. Salvage logging contributed to the harsh post-fire microsite environment by increasing soil temperature and reducing soil moisture. The presence of deadwood, instead, played a facilitative role in ameliorating microclimatic conditions for seedlings. The CR intervention had the highest soil moisture and the lowest soil temperature, which could be crucial for seedling survival in the first post-fire years. Due to its negative impact on microclimatic conditions affecting the availability of preferential microsites for regeneration recruitment, salvage logging should not be considered as the only intervention to be applied in post-fire environments. In the absence of threats or hazards requiring specific management actions (e.g., public safety, physical hazards for facilities), in the investigated ecosystems, no intervention, leaving all deadwood on site, could result in better microclimatic conditions for seedling establishment. A preferred strategy to speed-up natural processes and further increase safe sites for regeneration could be felling standing dead trees whilst releasing deadwood (at least partially) on the ground

High-dimensional detection of Landscape Dynamics: a Landsat time series-based algorithm for forest disturbance mapping and beyond

Time series analysis of medium-resolution multispectral satellite imagery is critical to investigate forest disturbance dynamics at the landscape scale. In particular, the spatial, temporal, and radiometric consistency of Landsat time series data provides unprecedented insight into past disturbances that occurred over the last four decades. Several Landsat time series-based algorithms have been developed to automate the detection of forest disturbances. However, automated detection of non-stand-replacing disturbances based on Landsat time series remains a challenging task due to the difficulty of effectively separating them from spectral noise. Here, we present the High-dimensional detection of Landscape Dynamics (HILANDYN) algorithm, which exploits spatial and spectral information provided by Landsat time series to detect forest disturbance dynamics retrospectively. A novel and unsupervised procedure for changepoint detection in high-dimensional time series allows HILANDYN to perform the temporal segmentation of inter-annual time series into linear trends. The algorithm embeds a noise filter to remove spurious changepoints caused by residual spectral noise in the time series. We tested HILANDYN to detect disturbances that occurred in the forests of the European Alps over a period of 39 years, i.e. between 1984 and 2022, and evaluated its accuracy using a validation dataset of 3000 plots randomly located inside and outside the disturbed patches. We compared HILANDYN with the Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST), which is a well-established and high-performing time series-based algorithm for changepoint detection. The quantitative results highlighted that the number of bands, i.e. original Landsat bands and spectral indices, included in the high-dimensional time series and the threshold controlling the significance of changepoints strongly influenced the user’s accuracy (UA). Conversely, changes in the combinations of bands primarily affected the producer’s accuracy (PA). HILANDYN achieved an F1 score of 0.801, which increased to 0.833 when we activated the noise filter, allowing the algorithm to balance UA (83.1%) and PA (83.5%). The qualitative results showed that disturbed forest patches detected by HILANDYN were characterized by a high spatio-temporal consistency, regardless of the disturbance severity. Furthermore, our algorithm was able to detect forest patches associated with secondary disturbances, such as salvage logging, that occur in close succession with respect to the primary event. The comparison with BEAST evidenced a similar sensitivity of the algorithms to non-stand-replacing events, as both achieved comparable PA. However, BEAST struggled to balance UA and PA when using a single parameter set, achieving a maximum F1 score of 0.717. Moreover, the computational efficiency of BEAST in processing high-dimensional time series was very limited due to its univariate nature based on the Bayesian approach. The adaptability of HILANDYN to detect a wide range of disturbance severities using a single parameter set and its computational efficiency in handling high-dimensional time series promotes its scalability to large study areas characterized by heterogeneous ecological conditions

Revegetation through seeding or planting: A worldwide systematic map

Roughly 2 billion ha of land are degraded and in need of ecological restoration worldwide. Active restoration frequently involves revegetation, which leads to the dilemma of whether to conduct direct seeding or to plant nursery-grown seedlings. The choice of revegetation method can regulate plant survival and performance, with economic implications that ultimately feed back to our capacity to conduct restoration. We followed a peer -reviewed protocol to develop a systematic map that collates, describes and catalogues the available studies on how seeding compares to planting in achieving restoration targets. We compiled a database with the charac-teristics of all retrieved studies, which can be searched to identify studies of particular locations and habitats, objectives of restoration, plant material, technical aspects, and outcomes measured. The search was made in eight languages and retrieved 3355 publications, of which 178 were retained. The systematic map identifies research gaps, such as a lack of studies in the global South, in tropical rainforests, and covering a long time period, which represent opportunities to expand field-based research. Additionally, many studies overlooked reporting on important technical aspects such as seed provenance and nursery cultivation methods, and others such as watering or seedling protection were more frequently applied for planting than for seeding, which limits our capacity to learn from past research. Most studies measured outcomes related to the target plants but avoided measuring general restoration outcomes or economic aspects. This represents a relevant gap in research, as the choice of revegetation method is greatly based on economic aspects and the achievement of restoration goals goes beyond the establishment of plants. Finally, we identified a substantial volume of studies conducted in temperate regions and over short periods (0-5 y). This research cluster calls for a future in-depth synthesis, potentially through meta-analysis, to reveal the overall balance between seeding and planting and assess whether the response to this question is mediated by species traits, environmental characteristics, or technical aspects. Besides identifying research clusters and gaps, the systematic map database allows managers to find the most relevant scientific literature on the appropriateness of seeding vs. planting for particular conditions, such as certain species or habitats