Our study evaluated the effectiveness of using eight pathways in combination for a complete to transition from fossil fuels to renewable energy by 2050. These pathways included renewable energy development; improving energy efficiency; increasing energy conservation; carbon taxes; more equitable balancing of human wellbeing and per capita energy use; cap and trade systems; carbon capture, utilization, and storage; and nuclear power development. We used the annual ‘British Petroleum statistical review of world energy 2021’ report as our primary database. Globally, fossil fuels, renewable (primarily hydro, wind and solar), nuclear energy accounted for 83%, 12.6%, and 6.3% of the total energy consumption in 2020. To achieve zero fossil fuel use by 2050, we found that renewable energy production will need to be increased by up to 6-fold or 8-fold if energy demand is held constant at, or increased 50% from, the 2020 energy demand level. Constraining 2050 world energy demand to a 25% increase over the 2020 level, improves the probability of achieving independence from fossil fuels. Improvements in energy efficiency need to accelerate beyond the current rate of ~1.5% per year. Aggressive application of energy conservation policies involving land use and taxation could potentially reduce world energy use by 10% or more by 2050. Our meta-analysis shows that the minimum level of per capita energy consumption that would allow 8 billion people to have a ‘Decent Living Standard’ is on average ~70 GJ per capita per year, which is 93% of the 2020 global average. Developed countries in temperate climates with high vehicle-dependency needed ~120 GJ per capita year⁻¹, whereas equatorial countries with low vehicle-dependency needed 30 GJ per capita year⁻¹. Our meta-analyses indicated replacement of fossil fuels with renewable energy by 2050 may be possible but will require aggressive application of all eight pathways, major lifestyle changes in developed countries, and close cooperation among all countries.

We tested the Moment Distance Index (MDI) in combination with texture features for the summer vegetation mapping in the eastern Pamir Mountains, Tajikistan using the 2014 Landsat OLI (Operational Land Imager) image. The five major classes identified were sparse vegetation, medium-dense vegetation, dense vegetation, barren land, and water bodies. By utilizing object features in a random forest (RF) classifier, the overall classification accuracy of the land cover maps were 92% using a set of variables including texture features and MDI, and 84% using a set of variables including texture but without MDI. A decrease of the Kappa statistics, from 0.89 to 0.79, was observed when MDI was removed from the set of predictor variables. McNemar’s test showed that the increase in the classification accuracy due to the addition of MDI was statistically significant (p < 0.05). The proposed method provides an effective way of discriminating sparse vegetation from barren land in an arid environment, such as the Pamir Mountains.

Marco Polo sheep (Ovis ammon polii), listed as Near Threatened in the IUCN Red List, were surveyed using vehicles during three summers and three consecutive winters to determine the status and population structure of the subspecies in a hunted population in southeastern Pamirs, Tajikistan. A total of 8649, 8392, and 7663 sheep were observed in each winter survey with densities of 5.42, 4.91, and 4.49 argalis/km², respectively. The differences in numbers observed in different years were probably due principally to differing dates of surveys among years. The high ratios of lamb for every 100 ewe (53, 38, and 49 during the winter surveys, and 60, 45, and 58 during the summer surveys) and high percentages (45%–50%) of mature females during winter indicate that offspring recruitment is sufficient to maintain a stable population. The significant increase in the abundance of Marco Polo sheep in the Tajikistan Pamirs compared with that of previous population surveys may probably be attributed to low numbers of domestic livestock during the winter, low human population density, unfenced and widespread wild sheep habitats, and rigorous patrolling to limit poaching. The small number (45) of hunting permits has a minimal impact on the male sheep sector. Tajikistan now supports more argali than any other country.

The bighorn sheep in Mexico is classified as at-risk by the Mexican federal government. In the state of Baja California, wild sheep can be observed throughout the length of the state from the USA–Mexico border south to the Agua de Soda mountain range. This research aimed to document the historical trend of the bighorn population based on aerial surveys conducted in 1992, 1995, 1999, 2010, and 2021, and the abundance, distribution, and structure of bighorn sheep populations in Baja California, based on an aerial survey conducted from 8–14 November 2021, covering thirteen mountain ranges. The estimated sheep population in 2021 was based on the number of individuals observed; the sightability of the animals; the area sampled; and the total area of habitat available. In 30.5 flight hours, 456 bighorn sheep were observed, with an estimated population of 1697 ± 80 individuals. The observation rate was 16 sheep sighted per hour of flight, and the ram:ewe:lamb ratio was 62:100:19. When the results of the 2021 flight were compared to the results of the previous aerial surveys, there was a large variation between the data, which was related to the lack of consistency between the sampling designs used in each study. Nevertheless, a statistical test of the results of aerial surveys conducted in the state suggest that the Baja California bighorn sheep population remained stable between 1992 and 2021. This study highlights the need to standardize wild sheep aerial surveys by defining flight paths and establishing a consistent duration of flights. On the other hand, Baja California authorities should consider modifying the current conservation strategy for bighorn sheep to increase the species’ population in the state by initiating community-based wildlife conservation programs in rural communities.

We described in this report the essential geographic layers used as landscape drivers for the Marco Polo Argali habitat in the eastern Tajik Pamirs. Using remote sensing techniques and geographic information systems (GIS), individual layers were generated in order to acquire more information on argali patterns and habitat suitability and to make the dataset available online. We introduced an improved object-based image analysis in our mapping of the vegetation cover by utilizing spectral, topographic, and texture variables. We exhausted every Landsat image band and texture feature combination to select the best pairing of band-texture components. For vegetation class alone, the producer’s accuracy was 90.8% and the user’s accuracy was 91.6%.