Use of BP Neural Networks to Determine China’s Regional CO2 Emission Quota

China declared a long-term commitment at the United Nations General Assembly (UNGA) in 2020 to reduce CO2 emissions. This announcement has been described by Reuters as “the most important climate change commitment in years.” The allocation of China’s provincial CO2 emission quotas (hereafter referred to as quotas) is crucial for building a unified national carbon market, which is an important policy tool necessary to achieve carbon emissions reduction. In the present research, we used historical quota data of China’s carbon emission trading policy pilot areas from 2014 to 2017 to identify alternative features of corporate CO2 emissions and build a backpropagation neural network model (BP) to train the benchmark model. Later, we used the model to calculate the quotas for other regions, provided they implement the carbon emission trading policy. Finally, we added up the quotas to obtain the total national quota. Additionally, considering the perspective of carbon emission terminal, a new characteristic system of quota allocation was proposed in order to retrain BP including the following three aspects: enterprise production, household consumption, and regional environment. The results of the benchmark model and the new models were compared. This feature system not only builds a reasonable quota-related indicator framework but also perfectly matches China’s existing “bottom-up” total control quota approach. Compared with the previous literature, the present report proposes a quota allocation feature system closer to China’s policy and trains BP to obtain reasonable feature weights. The model is very important for the establishment of a unified national carbon emission trading market and the determination of regional quotas in China.

A Method for Projecting Features from Observed Sets of Phonological Classes

Given a set of phonological features, we can enumerate a set of phonological classes. Here we consider the inverse of this problem: given a set of phonological classes, can we derive a feature system? We show that this is indeed possible, using a collection of algorithms that assign features to a set of input classes and differ in terms of what types of features are permissible. This work bears on theories of both language-specific and universal features, provides testable predictions of the featurizations available to learners, and serves as a useful component in computational models of feature learning.

FEATURES OF THE "VERBAL PORTRAIT" OF CENTRAL AND SOUTHERN CAUCASIANS OF RUSSIA, COMPILED FROM THE BACK OF THE HEAD

The article is devoted to the search for differential diagnostic features of appearance that allow an expert level to judge whether an unknown person belongs to the Central or southern local Caucasian race, which is important in the practice of identity identification. Research material: verbal description of the appearance of 116 Central and 64 southern Europeans (Russians, Ukrainians, Belarusians; Avars, Abkhazians, Azerbaijanis, Armenians, Dargins, Ingush, Kabardins, Lezgins, Ossetians, etc.). The description of the appearance was made from the back of the head according to the original method, which includes 15 features. Methods of variation statistics were used to process the data obtained. Diagnostic coefficients were calculated for statistically significantly different values (p<0.05). It was found that black hair (8,2), low level of hair growth (3,1), post-traumatic deformation of the auricles (2,1), and hair growth in a counterclockwise curl (2,0) are the most typical for southern Caucasians. Central Caucasians are characterized by light brown (-14,7) or light brown hair (-4,1), moles or freckles on the back of the head or neck (-3,4) egg-shaped head (-2,1), high hair growth (-2,1). It is shown that the set of features allows us to achieve a practically reliable level of solving the problem of the local racial type of an unknown person. A distinctive feature and advantage of the developed feature system is the ability to solve an expert problem in situations where the most informative area in terms of race and diagnostics-the face – is destroyed (if we are talking about an unidentified corpse) or hidden from CCTV cameras (if we are talking about a criminal hiding his appearance).

Quantificational cycles and shifts

The chapter reports an inter-genetic diachronic study of quantificational particles, drawing from Indo-European and Japonic and making a case for diachronic typological approach to the syntax/semantics/pragmatics of quantificational meanings motivating a treatment of unidirectional semi- or fully cyclical change. Empirically, the quantificational expressions under investigation conform to the bimorphemic expression that comprises a wh-stem and a quantification particle (dubbed ‘superparticle’), e.g. *kwe in Proto-IE, and mo in Old Japanese. The grammaticalization of scalar universal quantifiers into negative polarity items (NPIs) in the history of Japonic is presented using a single feature-system change. What is more, the same feature system is assumed to underlie the aetiology of the ‘quantifier split’ in Indo-European. Theoretically, to present the fully explanatory view of the quantificational shifts and cycles, a novel model of a syntactico-centric pragmatics of grammaticized implicatures (Chierchia et al., 2012; Chierchia, 2013) is assumed.