Volume 2 Issue 1 (2026): In Progress

Recent legislative reforms in Italy have reshaped the legal framework governing agrivoltaic installations, particularly following Law No. 4 of 15 January 2026, which converted Decree-Law No. 175 of 21 November 2025 and amended the Renewable Energy Sources Consolidated Act (Legislative Decree No. 190/2024). This article examines how the reform affects the legal classification of agrivoltaic projects and the conditions for their authorisation on agricultural land. The analysis adopts a doctrinal legal method based on statutory interpretation, review of relevant administrative materials, and comparison between the new provisions and the previous regime. It argues that the reform consolidates agrivoltaics as an autonomous legal category of integrated land use, distinct from ground-mounted photovoltaic installations, and introduces a more selective regulatory model. Key innovations include the strengthening of agricultural continuity as a substantive legal requirement, the introduction of a certified professional declaration linked to a minimum threshold of 80% of Gross Saleable Production (GSP), enhanced documentary obligations during the authorisation phase, post-installation municipal monitoring, and quantitative limits linked to the calculation of suitable agricultural areas. These measures increase the evidentiary burden on project developers and agricultural undertakings while reinforcing the protection of productive agricultural land. The article concludes that the reform establishes a more rigorous balance between renewable energy deployment and land preservation, although its practical effectiveness will depend on the development of consistent administrative practices capable of limiting territorial divergence and litigation.

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Bangladesh is emerging as a key case for climate-resilient agriculture in circumstances of land scarcity and environmental stress. This review collates existing evidence towards understanding the role of sustainable agriculture practices and land restoration interventions in supporting food security and resilience. Systematic literature review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, integrating peer-reviewed studies and institutional reports. The system settings include four main interventions: climate-smart crop diversification, improved soil fertility management, efficient water resource management, and ecosystem restoration. Our findings demonstrate that, although these interventions led to higher productivity and adaptive capacity of beneficiaries as compared to non-beneficiaries, their effectiveness differs greatly across agro-ecological zones due to differences in access to inputs, institutional support and adoption by farmers. In addition to descriptive synthesis, this study employs a comparative and policy-oriented perspective by highlighting the technological, ecological and institutional dimensions of agricultural transformation. The results highlight that no single intervention is sufficient, and if anything, a coordinated and adaptive approach is necessary in order to make a sustainable impact. In the end, Bangladesh’s experience is transferable to other climate-vulnerable areas in how integrated and goal-oriented approaches can lead to adjusted resilience outcomes for smallholder farming systems, achieving productivity vs. environment sustainability balance with strong governance systems and sound extension service delivery.

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Mangroves are critical for climate change adaptation but face increasing threats from hurricanes and anthropogenic pressures. Quantitative baselines for post-disturbance conditions remain limited in Cuba. We integrated field forest inventories (40 permanent 100 m² plots) with remote sensing time series (aerial photographs: 1957, 1970, 1999; Landsat 7: 2003; Sentinel-2A: 2022, 2025) in La Coloma, southwestern Pinar del Río, Cuba. Biophysical variables (diameter, height, basal area, ecological importance value index) and seven spectral indices (Normalized Difference Vegetation Index (NDVI), Mangrove Vegetation Index (MVI), Green Cover Index (GCI), Enhanced Vegetation Index-2 (EVI-2), Normalized Difference Salinity Index (NDSI), Normalized Difference Moisture Index (NDMI), Natural Regeneration Index (IRN)) were analyzed. Classification accuracy was assessed using confusion matrix and Kappa coefficient. The mangrove forest presents low-stature structure (mean height: 4.16 m; mean diameter at 1.30 m: 5.41 cm). Total basal area was 7.41 m²·ha⁻¹. Hurricane Ian (September 2022) affected 54% of individuals (351 trees). Mangrove cover increased from 6,434 ha (1957) to 7,282 ha (2022), a net increase of 848 ha (11.64%). Spectral indices revealed progressive degradation: MVI confirmed an alarming 161.8% increase (154.0 ha) in moderately degraded areas. Overall, 403.5 ha (33% of the total analyzed area) were degraded (199.4 ha highly degraded, 204.2 ha degraded), with 288.6 ha regenerating and 546.6 ha healthy. Classification accuracy was 87.3% (Kappa = 0.84). Six anthropogenic and three natural stressors were identified, including the defoliating lepidopteran Junonia genoveva affecting 80% of sampled areas. Integrating field inventories with Sentinel-2 remote sensing and GIS (Geographic Information System) enables precise post-disturbance mangrove diagnosis. The established baseline serves as a predictive tool for land-use planning and assisted restoration prioritization under Cuba's "Tarea Vida" climate adaptation plan.

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Classifying land use and land cover (LULC) is a fundamental process in remote sensing and geographical information systems (GIS) that is essential to many applications, including disaster assessment, urban planning, environmental monitoring, and natural resource management. Understanding the dynamics of landscapes and how they evolve over time requires accurate classification of land use and land cover groups. For this reason, straightforward classification methods like decision trees, artificial neural networks and maximum likelihood have historically been employed extensively. However, there has been an increasing interest in investigating machine learning techniques' potential to enhance the precision and effectiveness of LULC classification since their introduction. Computer vision, natural language processing, and remote sensing are just a few of the fields that have greatly benefited from the quick development of machine learning algorithms, especially deep learning approaches. Due to its capacity to automatically extract intricate patterns and features from massive datasets, machine learning-based techniques have become more and more popular in LULC classification jobs in recent years, potentially surpassing conventional approaches. This research paper aims to conduct a Land use classification by using machine learning based (ML) models (Support Vector Machine (SVM) model, Random Forest (RF) and Decision Trees (DT) models) with the use of open-sourced Python modules (Rasterio, Numpy, and Scikit-learn). The comparative analysis demonstrates that the SVM model achieved the highest performance with an Overall Accuracy (OA) of 97.30%, followed by Random Forest at 94.59%, and Decision Tree at 89.19%.

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