This study examines conditions that impact PPP delivery success or failure in the roadways sector in India using Qualitative Comparative Analysis. QCA is well-suited for problems where multiple factors combine to create pathways leading to an outcome. Past investigations have compared PPP and non-PPP project delivery performance, but this study examines performance within PPPs by uncovering a set of conditions that combine to influence the success or failure road PPP project delivery in India. Based on data from 21 cases, pathways explaining project delivery success or failure were identified. Specifically, PPPs with high concessionaire equity investment and low regional industrial activity led to project delivery success. Projects with lower concessionaire equity investment and low reliance on toll revenue and with either: (a) high project technical complexity or (b) high regional industrial activity, led to project delivery failure. The pathways identified did not have coverage values that they were extremely strong. Coverage strength was hindered by lack of access to information on additional conditions that could be configurationally important. Further, certain characteristics of the Indian market limit generalization. Identification of combinations of conditions leading to PPP project delivery success or failure improves knowledge of the impacts of structure and characteristics of these complex arrangements. This study is one of the first to use fuzzy QCA to understand project delivery success/failure in road PPP projects. Moreover, this study takes into account factors specific to a sector and delivery mode to explain project delivery performance.

In marginalized ecosystem-dependent rural communities, access to ecosystem services plays a crucial role in achieving sustainable livelihoods. This study was conducted to find out the influence of various livelihood capital components on the access mechanism for forest-based Provisioning Services (PS) in some selected villages of the Gosaba Block on the fringes of the Sundarban. The contribution of the livelihood capitals to gain access to Provisioning Services (PS) was identified using factor analysis on 160 households, selected through cluster random sampling. The sustainability levels of livelihood capitals were analyzed using the Prescott-Allen method (2001). The natural, financial, social, and physical capitals were significantly below average, while the human capital was close to average. Enhancement of human, physical, financial, and social capital, ease in issuing Biometric Fisherman cards for entering forests, flexibility in borrowing loans, and ecotourism by involving local villagers must be encouraged to enhance forest-based provisioning services in the near future.

Mapping land use and land cover (LULC) is essential for comprehending changes in the environment and promoting sustainable planning. To achieve accurate and effective LULC mapping, this work investigates the integration of Geographic Information Systems (GIS) with Machine Learning (ML) methodology. Different types of land covers in the Lucknow district were classified using the Random Forest (RF) algorithm and Landsat satellite images. Since the research area consists of a variety of landforms, there are issues with classification accuracy. These challenges are met by combining supplementary data into the GIS framework and adjusting algorithm parameters like selection of cloud free images and homogeneous training samples. The result demonstrates a net increase of 484.59 km² in built-up areas. A net decrement of 75.44 km² was observed in forest areas. A drastic net decrease of 674.52 km² was observed for wetlands. Most of the wastelands have been converted into urban areas and agricultural land based on their suitability with settlements or crops. The classifications achieved an overall accuracy near 90%. This strategy provides a reliable way to track changes in land cover, supporting resource management, urban planning, and environmental preservation. The results highlight how sophisticated computational methods can enhance the accuracy of LULC evaluations.

A large number of people of the fringe areas of Sundarban enter into the forests every year and encounter with the tigers simply for their livelihood. This study attempts to examine the extent and impact of human-animal conflicts in the Sundarban Reserve Forest (SRF) area in West Bengal, India. An intensive study of the data of the victims (both death and injury) between 1999 and 2014 reveals that, fishermen crab collector, honey collectors and woodcutters are generally victimized by the tiger attack. Pre monsoon period (April to June) and early winter period (Jan to March) are noted for the two-peak periods for casualties. Maximum casualty occurs between 8-10 am, and 2-4 pm. Jhilla (21.1%), Pirkhali (19.72 %), Chandkhali (11.72%), and Arbesi (9.35%) are the four most vulnerable forest blocks accounting more than 60 per cent occurrence of incidences. 67.24 per cent of the tiger attack victims were residents of Gosaba followed by Hingalganja (15%) and Basanti, (9.76%). The vulnerability rating puts the risk of tiger attack to 0.88 for every 10,000 residents of Gosaba block followed by 0.33 at Hingalganj Block and 0.11 at Bansanti Block. The majority of the victims (68%) were found to be males, aged between 30 and 50 years.