The Earth’s Population Can Reach 14 Billion in the 23rd Century without Significant Adverse Effects on Survivability

Abstract

This paper presents the results obtained from the study of the sustainable state between nature and human society on a global scale, focusing on the most critical interactions between the natural and anthropogenic processes. Apart from the conventional global models, the basic tool employed herein is the newly proposed complex model entitled “nature-society system (NSS) model”, through which a reliable modeling of the processes taking place in the global climate-nature-society system (CNSS) is achieved. This universal tool is mainly based on the information technology that allows the adaptive conformance of the parametric and functional space of this model. The structure of this model includes the global biogeochemical cycles, the hydrological cycle, the demographic processes and a simple climate model. In this model, the survivability indicator is used as a criterion for the survival of humanity, which defines a trend in the dynamics of the total biomass of the biosphere, taking into account the trends of the biocomplexity dynamics of the land and hydrosphere ecosystems. It should be stressed that there are no other complex global models comparable to those of the CNSS model developed here. The potential of this global model is demonstrated through specific examples in which the classification of the terrestrial ecosystem is accomplished by separating 30 soil-plant formations for geographic pixels 4° × 5°. In addition, humanity is considered to be represented by three groups of economic development status (high, transition, developing) and the World Ocean is parameterized by three latitude zones (low, middle, high). The modelling results obtained show the dynamics of the CNSS at the beginning of the 23rd century, according to which the world population can reach the level of 14 billion without the occurrence of major negative impacts.

A new big data approach based on geoecological information-modeling system

“…Certainly, this paper cannot solve this task. It is necessary to realize series of simulation experiments with consideration of reasonable scenarios (Krapivin et al, 2017b).…”

“…Figure 4 demonstrates the general structure of GIMS as the aggregate approaches, instruments, and methods for the processing of structured and unstructured data characterized by big volumes and significant variety. GIMS allows the combined use of different approaches to the processing of big data fluxes that primarily solve a new decision-making process to optimize these fluxes at the expense of effective monitoring alternatives and data processing tools (Krapivin, Varotsos, & Soldatov, 2017a, 2017b. Table 1.…”

“…Model of long-range atmospheric transport of the pollutants (Kondratyev et al, 2002) FPM Food production model (Dao et al, 2015;Nitu, Dumitrasku, Krapivin, & Mkrtchyan, 2015) aiFi Evolutionary algorithm for the indicator calculation of the food industry (Nitu et al, 2004) UEM an upwelling ecosystem model (Krapivin & Varotsos, 2016) MWEL Model of the typical water ecosystem on the land (Krapivin & Shutko, 2012) ahiS an algorithm for the human indicator survivability calculation (Krapivin et al, 2017b) DMEP Dynamic model of the environmental pollutants (Kondratyev et al, 2006) BDP the big data processing with the use of sequential and cluster analyses Soldatov, 2010Soldatov, , 2015) gSa the giMS structure adaptation to the simulation experiment conditions ) DFM Big data cloud formation and management SS Synthesis of the scenarios for the interaction of population with the environment SEMC Simulation experiment management and control…”

Water harvesting from fog using building envelopes: part I

“…In fact, hydrological cycle alteration are expected to generate significant modification in water resources spatial distribution over the years, including but not limited to drinking water (Krapivin et al . ). The exploration of alternative sources of potable water has led to the development of various innovative techniques, such as desalination (Karagiannis & Soldatos ) or cloud seeding (Boucher ).…”

“…From the 1950s, withdrawal amounts from both renewable and nonrenewable sources have constantly risen to meet increasing demands (Postel 2000). This increase is also related to a steady growth in food production registered over the last 50 years, which in turn recalls for an analogous trend in water demand (Krapivin et al 2017). In this framework, investigating the potential for water recovery from alternative sources to rain, rivers and groundwater is crucial to allow for more a sustainable and reasonable management of existing natural water resources.…”