This article aims to identify factors associated with distribution of Presidential grants for environmental initiatives in 2017-23. The main  assumption is to check if there is a presence of political logic in supporting regional eco-initiatives. While previous studies considered environmental conflicts, as a rule, on the part of eco-activists, in this paper we focus to a greater extent on studying the strategy of the authorities. For this purpose, we propose using the concept of regional environmental politics. Assuming that the key point for the authorities is an attempt to reduce eco-protest activity, we proceed from the fact that the authorities seek to use not only the "stick", meaning forceful strategies for combating protests, but also the "carrot", or strategies for co-opting eco-activists. In this model, the distribution of grants to support environmental initiatives is considered as a reaction to the nature of environmental problems and conflicts. We test three main hypotheses about the logic of project support: a reaction to the deterioration of the environmental situation in the region, a reaction to the frequency of eco-protests, and a reaction to inequality, which contributes to protest sentiments in general. We rely on several sources of empirical data. First, we use data on the distribution of Presidential grants to eco-NGOs. Second, we use a large-scale database on eco-protests. Third, we use data from the National Ecological Rating of Russian regions. Fixed-effects models show that the distribution of presidential grants follows both eco-protests and the deterioration of the environmental situation in the regions. In addition, the distribution of grants is affected by low inequality, which in Russia is typically characteristic of poor regions. We also illustrate our arguments on the example of the Sverdlovsk region, which indicates a “gross” rather than individually based nature of support for environmental initiatives.

Recent studies have shown that the validated dimensions in the best-known models of national culture converge into a two-dimensional default model, yielding a cultural map of the world reminiscent of the geographic one. The revised Minkov-Hofstede model is very similar to that default, whereas Inglehart-Welzel’s model is a rotated and flipped variant of it. However, another popular model - Schwartz’s - differs from the default: it does not have a dimension capturing the cultural contrast between East Asia and Latin America plus Africa. Consequently, it cannot explain national differences in educational achievement and a number of other important national indicators, relevant in international business. This omission in Schwartz’s model is puzzling as its author claims to have analyzed all values with invariant meanings across the world. On the other hand, Schwartz’s model has an idiosyncratic “mastery-harmony” dimension that is not consistent with any geo-economic pattern and has poor predictive properties, constituting another weakness. We show that these idiosyncrasies of Schwartz’s model stem from Schwartz’s controversial decision to ipsatize his items and use multidimensional scaling: a method which, even without ipsatization, can create spatial opposites of items that are not negatively correlated. A principal component analysis of raw (non-ipsatized) Schwartz value domains does yield a variant of the default model of culture. We argue that although ipsatizing Schwartz value measures is not wrong in an absolute sense, it yields an impoverished and somewhat puzzling image of cultural differences across the globe, whereas raw measures reproduce the Minkov-Hofstede variant of the default model relatively well, although a different selection of values might perform even better.

This paper inspects the changes in basic human values (“Schwartz values”) during the coronavirus pandemic, as well as analyzes the associations between values and negative pandemic experiences. Using the 4 waves of the panel online survey ‘Values in Crisis’ conducted in 2020-2022 in Russia, the study highlights considerable fluctuations in value orientations throughout the pandemic. The findings demonstrate a significant increase in conservation values (tradition), stimulation, and benevolence and a decline in hedonism and universalism by the end of the period. Multilevel regression models indicate positive associations of actual negative pandemic experiences and openness to change values and a reverse relationship with other value dimensions. As for prospective negative outcomes, concerns about health complications lower openness to change and universalism while increasing conservation. On the contrary, concerns about economic crisis are negatively related to tradition and positively – to self-enhancement and universalism. Further, the impact of health-related experiences decreases over time, whereas economic-related issues become more important.

When and how does an environmental protest cycle affect election outcomes under electoral authoritarianism? Drawing on the case of the Bashkortostan republic, a Russian ethnic region, I leverage the spatial proximity to the protest site to identify its effects on parliamentary elections. The environmental protest cycle peaked in Bashkortostan around the regional government’s decisions to extract minerals from shikhans – mountains composed of limestone. Employing a difference-in-differences (DiD) design, I show that precincts exposed to the environmental protest cycle experienced a significant drop in United Russia vote share, while voting for systemic opposition increased in the affected precincts. To explain the dynamics, I propose treating non-political protests, such as environmental ones, as an information revelation mechanism. The mechanism identified through a causal mediation analysis indicates that the environmental protest cycle conveys information on regional malperformance to voters. Their updated preferences, in turn, heavily undermine the electoral mobilizing capacity of local elites.

Cross-cultural research in social and behavioral sciences has expanded hugely over the past 50 years, but progress is currently hampered by a lack of appreciation of the profoundly differing principles and goals of two distinct traditions. The first is the main variant of cross-cultural psychology (CCP), focusing on how culture shapes individual psychological functioning. The second was pioneered by Hofstede. It studies societal differences, and we name it “comparative culturology” (CC). We explain how these two paradigms differ. CCP is grounded in psychology and typically looks for unobservable individual-level constructs, which supposedly exist independently of their measurement, to provide understanding of individual differences as affected by culture. CC is an interdisciplinary field whose roots and impact span sociology, anthropology, political science, economics, management studies, psychology, and beyond. CC measures cultural dimensions as group-level constructs created by researchers, which are best understood as ecological manifolds: conglomerates of conceptually and statistically associated variables (not necessarily held together by a single underlying factor) that collectively explain national (and other group) differences. Given these paradigmatic distinctions, the two fields need not, and cannot, use the same validation methods. They should co-exist and collaborate based on mutual appreciation of their differences, without attempts by either field to impose its idiosyncrasies on the other.

Increased public awareness of environmental problems and the strengthening of diverse identities may lead to the emergence of “ethnic environmentalism.” These are movements that make environmental claims associated with the interests of an ethnic community. We develop this concept by examining protests in the Russian regions. Our findings suggest that ethnic identity facilitates environmental activism under the following conditions: the ethnic group claims a special cultural relationship to the natural environment; environmental concern and ethnic identity are linked to the unfair distribution of resources; and blame is directed toward regional actors, rather than the federal government.

Some 40 years ago, Dutch social scientist Geert Hofstede laid the foundations of the science of modern cultural comparisons and created the most popular model of national culture, still in use today across the world. Meanwhile, numerous issues with that model have been identified, and the need for a thorough revision has become obvious. This chapter briefly explains the Hofstede model and its issues and summarizes the existing revisions of it, resulting in a new, simpler, and more robust Minkov–Hofstede model. This new version explains a wide range of differences in national indicators, such as transparency-corruption, gender equality, road death tolls and industrial fatalities, educational achievement, violent crime, adolescent fertility, family structure, and innovation rates, to name just a few. These indicators form a pattern that is similar to the new Minkov–Hofstede model and can be explained through similar theories. This is evidence that subjective culture (what people say they think and feel) has a mirror image in objective culture (what people do). The new Minkov–Hofstede model emerges from analyses of countries, as well as from some subnational units, such as US states.

Background

Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations.

Methods

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds.

Findings

The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles.

Interpretation

Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere.

This article examines the attitudes of contemporary Russian conservatives toward “the green agenda.” Although the topic of ecology was originally considered a priority of left-wing parties and movements, in recent years right-wing European politicians have been increasingly discussing and using environmental issues to boost their popularity. While the left-wing green agenda largely focused on global issues, such as climate change, cutting greenhouse gas emissions, and energy transition, right-wing populists, denying the importance of the declared environmental policy, proposed focusing on local problems – preserving local nature and national control over natural resources. This trend can now be observed in Russia: domestic politicians actively discuss the green agenda, offering their evaluations and ways to solve environmental problems. This article pursues a twofold aim. First, to show that the Western and Russian conservative discourses have more similarities than differences, the Russian green political discourse tracing its roots both to the Russian environmental movement and to European populists. Secondly, we investigate whether the views of conservatives influence environmental policies in Russia. To this end, we studied data on presidential environmental grants supported for 2018-2022. A total of more than 4,000 applications were analyzed. The results show that the majority of projects have a local character. Patriotic organizations are getting more involved in environmental issues, with several environmental organizations deliberately starting to use patriotic rhetoric. The results seem to indicate traces of conservative attitudes in environmental policy.